Tech Tutorials

Getting Started with Python and Redis

This is a small tutorial to start using Redis with Python. We will look at the steps that tell you how to install Redis in your local ubuntu machine. As we are compiling from source this should also represent how to install in any linux distribution. Then, we can look at some basic commands in Redis and get a feel of the Redis commands. Finally, we will install the Redis-py module and see how we can interface with the Redis module from Python.

Before going through this tutorial, please go through our Redis infographic to get an overview of the Redis module and how to approach it. We also have a webinar on developing a Redis module. I am using an ubuntu machine. Let us start with the installation of Redis.

sudo apt-get update

Install build essentials which have C and C++ compilers and other GNU C libraries which will help us in the installation later.

sudo apt-get install build-essential

Tcl is also needed to run Redis.

sudo apt-get install tcl8.5

Download the Redis source and untar it.

wget http://download.redis.io/releases/redis-stable.tar.gz

tar xzf redis-stable.tar.gz

cd redis-stable

We will need to now compile from source.

make 

make test

make install

Once this is done, there will be a set of scripts that will be created in the utils folder which can be used to install the service.

?  utils sudo ./install_server.sh

Welcome to the redis service installer

This script will help you easily set up a running redis server.





Please select the redis port for this instance: [6379]

Selecting default: 6379

Please select the redis config file name [/etc/redis/6379.conf]

Selected default - /etc/redis/6379.conf

Please select the redis log file name [/var/log/redis_6379.log]

Selected default - /var/log/redis_6379.log

Please select the data directory for this instance [/var/lib/redis/6379]

Selected default - /var/lib/redis/6379

Please select the redis executable path [/usr/local/bin/redis-server]

Selected config:

Port           : 6379

Config file    : /etc/redis/6379.conf

Log file       : /var/log/redis_6379.log

Data dir       : /var/lib/redis/6379

Executable     : /usr/local/bin/redis-server

Cli Executable : /usr/local/bin/redis-cli

Is this ok? Then press ENTER to go on or Ctrl-C to abort.

Copied /tmp/6379.conf => /etc/init.d/redis_6379

Installing service...

 Adding system startup for /etc/init.d/redis_6379 ...

   /etc/rc0.d/K20redis_6379 -> ../init.d/redis_6379

   /etc/rc1.d/K20redis_6379 -> ../init.d/redis_6379

   /etc/rc6.d/K20redis_6379 -> ../init.d/redis_6379

   /etc/rc2.d/S20redis_6379 -> ../init.d/redis_6379

   /etc/rc3.d/S20redis_6379 -> ../init.d/redis_6379

   /etc/rc4.d/S20redis_6379 -> ../init.d/redis_6379

   /etc/rc5.d/S20redis_6379 -> ../init.d/redis_6379

Success!

Starting Redis server...

Installation successful!

To access Redis, we will need the redis-cli.

?  utils redis-cli

127.0.0.1:6379> exit

We can set and get key values using the “SET” and “GET” keywords.

127.0.0.1:6379> SET users:GeorgeWashington "lang: python, born:1990"

OK

127.0.0.1:6379> GET users:GeorgeWashington

"lang: python, born:1990"

127.0.0.1:6379> exit

Let's now install the Python module and try to see if we can access the Redis server from Python. Let's test the Redis server with the Python.

?  redis_tutorial virtualenv venv -p python3.5

Running virtualenv with interpreter /usr/bin/python3.5

Using base prefix '/usr'

New python executable in venv/bin/python3.5

Also creating executable in venv/bin/python

Installing setuptools, pip...done.

?  redis_tutorial source venv/bin/activate

(venv)?  redis_tutorial

(venv)?  redis_tutorial

(venv)?  redis_tutorial pip install redis

Downloading/unpacking redis

  Downloading redis-2.10.5-py2.py3-none-any.whl (60kB): 60kB downloaded

Installing collected packages: redis

Successfully installed redis

Cleaning up...

(venv)?  redis_tutorial python

Python 3.5.2 (default, Jul 17 2016, 00:00:00)

[GCC 4.8.4] on linux

Type "help", "copyright", "credits" or "license" for more information.

>>> import redis

>>> r = redis.StrictRedis()

>>> r.get("mykey")

>>> r.get("mykey")

>>> r.get("users:GeorgeWashington")

b'lang: python, born:1990'

This was a basic tutorial about using Python with Redis. Next, we will lay a frontend for making a twitter client using Flask as the middleware and Redis in the backend.

References:

agiliq, getting started with redis and python
digitalocean, how to install and use redis

Tech Tutorials

Beginners Guide to Regression Analysis and Plot Interpretations

Introduction

"The road to machine learning starts with Regression. Are you ready?"

If you are aspiring to become a data scientist, regression is the first algorithm you need to ~~learn~~ master. Not just to clear job interviews, but to solve real-world problems. Many consultancy firms continue to use regression techniques on a large scale to help their clients. While it's one of the easiest algorithms to learn, it requires persistent effort to master.

Running a regression model is simple. A basic model <- y ~ x does the job. But optimizing it for better accuracy is the real challenge. If your model gives adjusted R² = 0.678, how will you improve it?

This article introduces key regression concepts with practical applications in R. You’ll learn to build, improve, and optimize regression models. The focus here is on linear and multiple regression.

Note: Best suited for readers new to machine learning who have some knowledge of statistics. R should be installed.

What is Regression? How does it work?
What are the assumptions made in Regression?
How do I know if these assumptions are violated in my data?
How can I improve the accuracy of a Regression Model?
How can I assess the fit of a Regression Model?
Practice Time – Solving a Regression Problem

Tech Tutorials

Collections and Defaultdict in Python

NSA whistleblower in exile, Edward Snowden, talks about how FBI could have reviewed 650K emails in less than 8 days!

@jeffjarvis Drop non-responsive To:/CC:/BCC:, hash both sets, then subtract those that match. Old laptops could do it in minutes-to-hours.
— Edward Snowden (@Snowden) November 7, 2016

Snowden says the FBI could have used hashing to identify emails that were not copies of ones they had already seen. Few things capture people’s interest like alleged conspiracies and political intrigue, yes? I’m no different. But what interests more is hashing. Touted by many as the “greatest idea in programming,” hashing, which involves the hash function, helps you find, say A, stored somewhere, say B. For example, the organizing and accessing of names and numbers in your “can’t bear to be parted from" smartphone.

Hashing is a technique where a data-structure called the “hash map” is implemented. This structure is an associative array where specific keys are mapped to specific values. A hash function is then used to compute an index into an array of buckets or slots from which the desired value can be found. The result is that (key, value) lookups are extremely fast and more efficient than searches based on popular trees like BST. To get in-depth knowledge about hashing, I recommend that you can go through our “Basics of Hash Tables” in our practice section.

Almost all modern languages have hashing implemented at the language level. In Python, hashing is implemented using the dictionary data structure, which is one of the basic data structures a beginner in Python learns. If you have only been using the dict module implementation in your code, I suggest you look at other implementations like defaultdicts and ordereddicts and use them more frequently in your code. Here, we will look more closely into the defaultdict module.

Defaultdicts come in the Collections internal library. Collections contains alternatives to the general purpose Python containers like dict, set, list, and tuple. Kind of like the Dark Knight is the more interesting “implementation” of Bruce Wayne.

Defaultdict is subclassed from the built-in dict module. You may have encountered the following common uses cases for which you have been using the default container.

Building nested dicts or JSON type constructs:

JSON is a very popular data structure. One of the major use cases for a JSON is creating web APIs. JSON also neatly corresponds to our dict object. A sample JSON object could look like this.

{"menu":

    {"id": "file",

    "value": "File",

    "popup": {

        "menuitem": [

        {"value": "New", "onclick": "CreateNewDoc()"},

        {"value": "Open", "onclick": "OpenDoc()"},

        {"value": "Close", "onclick": "CloseDoc()"}

    ]}

}}

Source:http://json.org/example.html.

We cannot create a json file by using the following command; it will throw a KeyError.

some_dict = {}

some_dict["menu"]["popup"]["value"] = "New"

So, we will have to write complicated error handling code to handle this KeyError.

This way of writing is considered un-Pythonic. In its place, try out the following construct.

import collections

tree = lambda: collections.defaultdict(tree)

some_dict = tree()

# below will create non existent keys

some_dict["menu"]["popup"]["value"] = "New"

A defaultdict is initialized with a function (“default factory”) that takes no arguments and provides the default value for a non-existent key. A defaultdict will never raise a KeyError. Any key that does not exist gets the value returned by the default factory.

Please ensure that you pass function objects to defaultdict. Do not call the function, that is, defaultdict(func), not defaultdict(func()).

Let’s check out how it works.

ice_cream = collections.defaultdict(lambda: 'Vanilla')

ice_cream['Sarah'] = 'Chunky Monkey'

ice_cream['Abdul'] = 'Butter Pecan'

print(ice_cream['Sarah']) # out: 'Chunky Monkey'

print(ice_cream['Joe']) # out: 'Vanilla

Having cool default values:

Another fast and flexible use case is to use itertools.repeat() which can supply any constant value.

import itertools

def constant_factory(value):

    return itertools.repeat(value).next

d = collections.defaultdict(constant_factory(''))

d.update(name='John', action='ran')

print('%(name)s %(action)s to %(object)s' % d)

This should print out “John ran to.” As you can observe, the “object” variable gracefully defaulted to an empty string.

Performance:

Like you see in this stackoverflow post, we tried to do a similar benchmarking only between dicts(setdefault) and defaultdict. You can see it here: https://github.com/infinite-Joy/hacks/blob/master/defaultdict_benchmarking.ipynb

from collections import defaultdict



try:

    t=unichr(100)

except NameError:

    unichr=chr



def f1(li):

    '''defaultdict'''

    d = defaultdict(list)

    for k, v in li:

        d[k].append(v)

    return d.items()



def f2(li):

    '''setdefault'''

    d={}

    for k, v in li:

        d.setdefault(k, []).append(v)

    return d.items()





if __name__ == '__main__':

    import timeit

    import sys

    print(sys.version)

    few=[('yellow', 1), ('blue', 2), ('yellow', 3), ('blue', 4), ('red', 1)]

    fmt='{:>12}: {:10.2f} micro sec/call ({:,} elements, {:,} keys)'

    for tag, m, n in [('small',5,10000), ('medium',20,1000), ('bigger',1000,100), ('large',5000,10)]:

        for f in [f1,f2]:

            s = few*m

            res=timeit.timeit("{}(s)".format(f.__name__), setup="from __main__ import {}, s".format(f.__name__), number=n)

            st=fmt.format(f.__doc__, res/n*1000000, len(s), len(f(s)))

            print(st)

            s = [(unichr(i%0x10000),i) for i in range(1,len(s)+1)]

            res=timeit.timeit("{}(s)".format(f.__name__), setup="from __main__ import {}, s".format(f.__name__), number=n)

            st=fmt.format(f.__doc__, res/n*1000000, len(s), len(f(s)))

            print(st)

        print()

Below is the output that I got on my machine using Anaconda.

3.5.2 |Anaconda 4.1.1 (32-bit)| (default, Jul  5 2016, 11:45:57) [MSC v.1900 32 bit (Intel)]

 defaultdict:       5.48 micro sec/call (25 elements, 3 keys)

 defaultdict:      11.20 micro sec/call (25 elements, 25 keys)

  setdefault:       7.80 micro sec/call (25 elements, 3 keys)

  setdefault:       8.97 micro sec/call (25 elements, 25 keys)



 defaultdict:      14.66 micro sec/call (100 elements, 3 keys)

 defaultdict:      42.19 micro sec/call (100 elements, 100 keys)

  setdefault:      26.71 micro sec/call (100 elements, 3 keys)

  setdefault:      34.78 micro sec/call (100 elements, 100 keys)



 defaultdict:     623.21 micro sec/call (5,000 elements, 3 keys)

 defaultdict:    2207.91 micro sec/call (5,000 elements, 5,000 keys)

  setdefault:    1329.99 micro sec/call (5,000 elements, 3 keys)

  setdefault:    3076.57 micro sec/call (5,000 elements, 5,000 keys)



 defaultdict:    4625.00 micro sec/call (25,000 elements, 3 keys)

 defaultdict:   15950.98 micro sec/call (25,000 elements, 25,000 keys)

  setdefault:    6907.47 micro sec/call (25,000 elements, 3 keys)

  setdefault:   17605.08 micro sec/call (25,000 elements, 25,000 keys)

Following are the broad inferences that can be made from the data:

1. defaultdict is faster and simpler with small data sets.
2. defaultdict is faster for larger data sets with more homogenous key sets.
3. setdefault has an advantage over defaultdict if we consider more heterogeneous key sets.

Note: The results have been taken by running it on my machine with Python 3.5 implementation of Anaconda. I strongly recommend you to not follow these blindly. Do your own benchmarking tests with your own data before implementing your algorithm.

Now that we have discussed the DefaultDict module, I hope that you are already thinking of using it more and also refactoring your code base to implement this module more. Next, I’ll be coming up with a detailed discussion on the Counter module.

References:
stackoverflow, How are Python's Built In Dictionaries Implemented
stackoverflow, Is a Python dictionary an example of a hash table?e
python.org, Dictionary in Python
python.org, Python3 docs, collections — Container datatypes
python.org, Python2 docs, collections — Container datatypes
accelebrate, Using defaultdict in Python

Tech Tutorials

Algorithm on how to find the day of a week

In 1970, John Horton Conway devised an algorithm, known as the “Doomsday Algorithm,” to calculate the day of the week for any given date using mental math. It's easy to remember and doesn’t require a calculator.

This algorithm uses the formula: (d + m + y + [y/4] + c) mod 7

Here, d is the day, m is the month, y is the year, and c is the century number.

Each weekday is assigned a number using modulo 7. For instance:

Sunday = 1
Monday = 2
... and so on.

What Do We Know?

Common years have 365 days, and leap years have 366. Each week has 7 days. The modulo properties help identify patterns in calendar dates.

Since 365 mod 7 = 1, each new year starts a day ahead of the previous year (unless it's a leap year, which adds one more day shift).

Some months start on the same day of the week. For instance, in a common year, April and July start on the same day.

Corresponding Months

In common years:

January and October
February, March, and November
April and July
No month matches with August

In leap years:

January, April, and July
February and August
March and November
No month matches with October

Tomohiko Sakamoto's Optimization

Sakamoto created an optimized version of the Doomsday Algorithm considering leap years. For example:

January 31 + February 28 = 59 days → 59 mod 7 = 3
So, March 1 is 3 days ahead of January 1

To accommodate leap years accurately, use the formula:

y/4 - y/100 + y/400

This adjusts the extra day added every four years, removed every 100 years, and re-added every 400 years.

For months less than March, we subtract one from the year to adjust for leap year effect:

y -= m < 3

We also use a predefined list to handle the month values:

{0, 3, 2, 5, 0, 3, 5, 1, 4, 6, 2, 4}

C++ Code

int dow(int y, int m, int d)
{
  static int t[] = {0, 3, 2, 5, 0, 3, 5, 1, 4, 6, 2, 4};
  y -= m < 3;
  return (y + y/4 - y/100 + y/400 + t[m-1] + d) % 7;
}

Python Code

def day_of_week(year, month, day):
    t = [0, 3, 2, 5, 0, 3, 5, 1, 4, 6, 2, 4]
    year -= month < 3
    return (year + int(year/4) - int(year/100) + int(year/400) + t[month-1] + day) % 7

This algorithm provides a quick and efficient way to calculate the day of the week for any date using simple arithmetic and a small lookup table.

Tech Tutorials

PYTHON DIARIES CHAPTER 4 → FUNCTION PART - 1

If you’re new to Python, it’s recommended to read the Python Diaries - Chapter 1, Chapter 2, and Chapter 3 as we build on some concepts covered there.

What is a Function?

A function is a block of organized, reusable code that performs a single, related task. It enables modularity and code reuse. Think of a function as a black box—it takes input, processes it, and produces output.

Function Black Box

def name_of_function(arguments):
    # your code
    return result  # returning is optional

Example:

def sum_of_numbers(a, b):
    c = a + b
    return c

print(sum_of_numbers(2, 3))

Output: 5

Three main parts of a function:

Input
Logic/Code
Output

Input

Data provided to the function for processing. It can be passed via:

Parameters/arguments
Global variables
Memory location

Logic/Code

Statements that define what the function does with the input.

Logic can range from a single line to hundreds of lines depending on the task.

Output

The result of processing the input. Output can be provided via:

Return statement
Modification of a memory location
Print statement

Variable Scope

Local variables: Defined inside a function and inaccessible outside it.
Global variables: Declared outside all functions, accessible inside if declared with global.

Note: Global scope is module-wide. Built-in scope includes variables/literals defined by Python and usable anywhere in a program.

Returning Multiple Values

You can return multiple values from a function using a comma-separated list.

Example:

Given an array, find number K and return its index and the previous element (assume K is not at index 0 and elements are distinct).

array = [5, 6, 7, 12, 2, 4, 3, 9, 25, 29]
k = 29

def searching():
    global array
    global k
    array.sort()
    index = 0
    while index < len(array):
        if array[index] == k:
            return index, array[index - 1]
        index += 1

index, previous_value = searching()
print(index)
print(previous_value)

Output:

9
25

sort() vs sorted()

sort() modifies the original list in-place.
sorted() returns a new sorted list.

Example:

array = [3, 4, 1, 2, 5]
new_arr = sorted(array)

print("array ->", array)
print("new_arr ->", new_arr)

array_2 = [2, 3, 5, 4, 1]
array_2.sort()
print("array_2 ->", array_2)

Output:

array -> [3, 4, 1, 2, 5]
new_arr -> [1, 2, 3, 4, 5]
array_2 -> [1, 2, 3, 4, 5]

Returning multiple values as a tuple: If you use a single variable on the left-hand side when returning multiple values, Python will pack them into a tuple.

That wraps up the basics of functions in Python. More advanced topics will follow in the upcoming articles. Stay tuned!

Tech Tutorials

Designing a Logistic Regression Model

Data is key for making important business decisions. Depending upon the domain and complexity of the business, there can be many different purposes that a regression model can solve. It could be to optimize a business goal or find patterns that cannot be observed easily in raw data.

Even if you have a fair understanding of maths and statistics, using simple analytics tools you can only make intuitive observations. When more data is involved, it becomes difficult to visualize a correlation between multiple variables. You have to then rely on regression models to find patterns, which you can’t find manually, in the data.

In this article, we will explore different components of a data model and learn how to design a logistic regression model.

1. Logistic equation, DV, and IDVs

Before we start design a regression model, it is important to decide the business goal that you want to achieve with the analysis. It mostly revolves around minimizing or maximizing a specific (output) variable, which will be our Dependent variable (DV).

You must also understand the different metrics that are available or (in some cases) metrics that can be controlled to optimize the output. These metrics are called predictors or independent variables (IDVs).

A generalized linear regression equation can be represented as follows:

Y = β₀ + β₁X₁ + β₂X₂ + ... + βₚXₚ

where Xᵢ are the IDVs, βᵢ are the coefficients of the IDVs and β₀ is intercept.

This equation can be represented as follows:

Yᵢⱼ = Σₐ₌₁ᵖ Xᵢₐ βₐⱼ + eᵢⱼ

Logistic regression can be considered as a special case of linear regression where the DV is categorical or continuous. The output of this function is mostly probabilistic and lies between 0 to 1. Therefore, the equation of logistic regression can be represented in the exponential form as follows:

Y = 1 / (1 + e⁻ᶠ⁽ˣ⁾)

which is equivalent to:

Y = 1 / (1 + e⁻(β₀ + β₁X₁ + β₂X₂ + ... + βₚXₚ))

As you can see, the coefficients represent the contribution of the IDV to the DV. If Xᵢ is positive, then the high positive value increases the output probability whereas the high negative value of βᵢ decreases the output.

2. Identification of data

Once we fix our IDVs and DV, it is important to identify the data that is available at the point of decisioning. A relevant subset of this data can be the input of our equation, which will help us calculate the DV.

Two important aspects of the data are:

Timeline of the data
Mining the data

For example, if the data captures visits on a website, which has undergone suitable changes after a specific date, you might want to skip the past data for better decisioning. It is also important to rule out any null values or outliers, as required.

This can be achieved with a simple piece of code in R, which will have the following method:

Uploading the data from the .csv file and storing it as training.data. We shall use a sample data from imdb, which is available on Kaggle.

> training.data <- read.csv('movie_data.csv',header=T,na.strings=c(""))
> sapply(training.data,function(x) sum(is.na(x)))
color      director_name      num_critic_for_reviews      duration       director_facebook_likes  
19         104                50                          15             104
> training.data$imdb_score[is.na(training.data$imdb_score)] <- mean(training.data$imdb_score, na.rm=T)

You can also think of additional variables that can have a significant contribution to the DV of the model. Some variables may have a lesser contribution towards the final output. If the variables that you are thinking of are not readily available, then you can create them from the existing database.

When we are dealing with the non real-time data that we capture, we should be clear about how fast this data is captured so that it can provide a better understanding of the IDVs.

3. Analyzing the model

Building the model requires the following:

Identifying the training data on which you can train your model
Programming the model in any programming language, such as R, Python etc.

Once the model is created, you must validate the model and its efficiency on the existing data, which is of course different from the training data. To put it simply, it is estimating how your model will perform.

One efficient way of splitting the training and modelling data are the timelines. Assume that you have data from January to December, 2015. You can train the model on data from January to October, 2015. You can then use this model to determine the output on the data from November and December. Though you already know the output for November and December, you will still run the model to validate it.

You can arrange the data in chronological order and classify it as training and test data from the following array:

> train <- data[1:X,] 
> test <- data[X:last_value,]

Fitting the model includes obtaining coefficients of each of the predictors, z-value, and P-value etc. It estimates how close the estimated values of the IDVs in the equation are when compared to the original values.

We use the glm() function in R to fit the model. Here's how you can use it. The 'family' parameter that is used here is 'binomial'. You can also use 'poisson' depending upon the nature of the DV.

> reg_model <- glm(Y ~., family=binomial(link='logit'), data=train)
> summary(reg_model)
> glm(formula = Y ~ ., family = binomial(link = "logit"), data = train)

Let's use the following dataset which has 3 variables:

> trees
   Girth Height Volume
1    8.3     70   10.3
2    8.6     65   10.3
3    8.8     63   10.2
4   10.5     72   16.4
...

Fitting the model using the glm() function:

> data(trees)
> plot(Girth~Volume, data=trees)
> abline(model)
> plot(Volume~Girth, data=trees)
> model <- glm2(vol ~ gir, family=poisson(link="identity"))
> abline(model)
> model

Call:  glm2(formula = vol ~ gir, family = poisson(link = "identity"))

Coefficients:
(Intercept)          gir  
    -30.874        4.608  

Degrees of Freedom: 30 Total (i.e. Null);  29 Residual
Null Deviance:     247.3 
Residual Deviance: 16.54 	AIC: Inf

In this article, we have covered the importance of identifying the business objectives that should be optimized and the IDVs that can help us achieve this optimization. You also learned the following:

Some of the basic functions in R that can help us analyze the model
The glm() function that is used to fit the model
Finding the weights of the predictors with their standard deviation

In our next article, we will use larger data-sets and validate the model that we will build by using different parameters like the KS test.

Authors

Meet our Authors

Get to know the experts behind our content. From industry leaders to tech enthusiasts, our authors share valuable insights, trends, and expertise to keep you informed and inspired.

Ruehie Jaiya Karri

Kumari Trishya

Hiring Strategies

7 Tech Recruiting Trends To Watch Out For In 2024

The last couple of years transformed how the world works and the tech industry is no exception. Remote work, a candidate-driven market, and automation are some of the tech recruiting trends born out of the pandemic.

While accepting the new reality and adapting to it is the first step, keeping up with continuously changing hiring trends in technology is the bigger challenge right now.

What does 2024 hold for recruiters across the globe? What hiring practices would work best in this post-pandemic world? How do you stay on top of the changes in this industry?

The answers to these questions will paint a clearer picture of how to set up for success while recruiting tech talent this year.

7 tech recruiting trends for 2024

6 Tech Recruiting Trends To Watch Out For In 2022

Recruiters, we’ve got you covered. Here are the tech recruiting trends that will change the way you build tech teams in 2024.

Trend #1—Leverage data-driven recruiting

Data-driven recruiting strategies are the answer to effective talent sourcing and a streamlined hiring process.

Talent acquisition leaders need to use real-time analytics like pipeline growth metrics, offer acceptance rates, quality and cost of new hires, and candidate feedback scores to reduce manual work, improve processes, and hire the best talent.

The key to capitalizing on talent market trends in 2024 is data. It enables you to analyze what’s working and what needs refinement, leaving room for experimentation.

Trend #2—Have impactful employer branding

98% of recruiters believe promoting company culture helps sourcing efforts as seen in our 2021 State Of Developer Recruitment report.

Having a strong employer brand that supports a clear Employer Value Proposition (EVP) is crucial to influencing a candidate’s decision to work with your company. Perks like upskilling opportunities, remote work, and flexible hours are top EVPs that attract qualified candidates.

A clear EVP builds a culture of balance, mental health awareness, and flexibility—strengthening your employer brand with candidate-first policies.

Trend #3—Focus on candidate-driven market

The pandemic drastically increased the skills gap, making tech recruitment more challenging. With the severe shortage of tech talent, candidates now hold more power and can afford to be selective.

Competitive pay is no longer enough. Use data to understand what candidates want—work-life balance, remote options, learning opportunities—and adapt accordingly.

Recruiters need to think creatively to attract and retain top talent.

Recommended read: What NOT To Do When Recruiting Fresh Talent

Trend #4—Have a diversity and inclusion oriented company culture

Diversity and inclusion have become central to modern recruitment. While urgent hiring can delay D&I efforts, long-term success depends on inclusive teams. Our survey shows that 25.6% of HR professionals believe a diverse leadership team helps build stronger pipelines and reduces bias.

McKinsey’s Diversity Wins report confirms this: top-quartile gender-diverse companies see 25% higher profitability, and ethnically diverse teams show 36% higher returns.

It's refreshing to see the importance of an inclusive culture increasing across all job-seeking communities, especially in tech. This reiterates that D&I is a must-have, not just a good-to-have.

—Swetha Harikrishnan, Sr. HR Director, HackerEarth

Trend #5—Embed automation and AI into your recruitment systems

With the rise of AI tools like ChatGPT, automation is being adopted across every business function—including recruiting.

Manual communication with large candidate pools is inefficient. In 2024, recruitment automation and AI-powered platforms will automate candidate nurturing and communication, providing a more personalized experience while saving time.

Trend #6—Conduct remote interviews

With 32.5% of companies planning to stay remote, remote interviewing is here to stay.

Remote interviews expand access to global talent, reduce overhead costs, and increase flexibility—making the hiring process more efficient for both recruiters and candidates.

Trend #7—Be proactive in candidate engagement

Delayed responses or lack of updates can frustrate candidates and impact your brand. Proactive communication and engagement with both active and passive candidates are key to successful recruiting.

As recruitment evolves, proactive candidate engagement will become central to attracting and retaining talent. In 2023 and beyond, companies must engage both active and passive candidates through innovative strategies and technologies like chatbots and AI-powered systems. Building pipelines and nurturing relationships will enhance employer branding and ensure long-term hiring success.

—Narayani Gurunathan, CEO, PlaceNet Consultants

Recruiting Tech Talent Just Got Easier With HackerEarth

Recruiting qualified tech talent is tough—but we’re here to help. HackerEarth for Enterprises offers an all-in-one suite that simplifies sourcing, assessing, and interviewing developers.

Our tech recruiting platform enables you to:

Tap into a 6 million-strong developer community
Host custom hackathons to engage talent and boost your employer brand
Create online assessments to evaluate 80+ tech skills
Use dev-friendly IDEs and proctoring for reliable evaluations
Benchmark candidates against a global community
Conduct live coding interviews with FaceCode, our collaborative coding interview tool
Guide upskilling journeys via our Learning and Development platform
Integrate seamlessly with all leading ATS systems
Access 24/7 support with a 95% satisfaction score

Recommended read: The A-Zs Of Tech Recruiting - A Guide

Staying ahead of tech recruiting trends, improving hiring processes, and adapting to change is the way forward in 2024. Take note of the tips in this article and use them to build a future-ready hiring strategy.

Ready to streamline your tech recruiting? Try HackerEarth for Enterprises today.

Hiring Strategies

(Part 2) Essential Questions To Ask When Interviewing Developers In 2021

The first part of this blog stresses the importance of asking the right technical interview questions to assess a candidate’s coding skills. But that alone is not enough. If you want to hire the crème de la crème of the developer talent out there, you have to look for a well-rounded candidate.

Honest communication, empathy, and passion for their work are equally important as a candidate’s technical knowledge. Soft skills are like the cherry on top. They set the best of the candidates apart from the rest.

Re-examine how you are vetting your candidates. Identify the gaps in your interviews. Once you start addressing these gaps, you find developers who have the potential to be great. And those are exactly the kind of people that you want to work with!

Let’s get to it, shall we?

What constitutes a good interview question?

An ideal interview should reveal a candidate’s personality along with their technical knowledge. To formulate a comprehensive list of questions, keep in mind three important characteristics.

Questions are open-ended – questions like, “What are some of the programming languages you’re comfortable with,” instead of “Do you know this particular programming language” makes the candidate feel like they’re in control. It is also a chance to let them reply to your question in their own words.
They address the behavioral aspects of a candidate – ensure you have a few questions on your list that allow a candidate to describe a situation. A situation where a client was unhappy or a time when the developer learned a new technology. Such questions help you assess if the candidate is a good fit for the team.
There is no right or wrong answer – it is important to have a structured interview process in place. But this does not mean you have a list of standard answers in mind that you’re looking for. How candidates approach your questions shows you whether they have the makings of a successful candidate. Focus on that rather than on the actual answer itself.

Designing a conversation around these buckets of interview questions brings you to my next question, “What should you look for in each candidate to spot the best ones?”

Hire GREAT developers by asking the right questions

Before we dive deep into the interview questions, we have to think about a few things that have changed. COVID-19 has rendered working from home the new normal for the foreseeable future. As a recruiter, the onus falls upon you to understand whether the developer is comfortable working remotely and has the relevant resources to achieve maximum productivity.

#1 How do you plan your day?

Remote work gives employees the option to be flexible. You don’t have to clock in 9 hours a day as long as you get everything done on time. A developer who hasn’t always been working remotely, but has a routine in place, understands the pitfalls of working from home. It is easy to get distracted and having a schedule to fall back on ensures good productivity.

#2 Do you have experience using tools for collaboration and remote work?

Working from home reduces human interaction heavily. There is no way to just go up to your teammate’s desk and clarify issues. Virtual communication is key to getting work done. Look for what kind of remote working tools your candidate is familiar with and if they know what collaborative tools to use for different tasks.

Value-based interview questions to ask

We went around and spoke to our engineering team, and the recruiting team to see what questions they abide by; what they think makes any candidate tick.

The result? – a motley group of questions that aim to reveal the candidate’s soft skills, in addition to typical technical interview questions and test tasks.

#3 Please describe three recent projects that you worked on. What were the most interesting and challenging parts?

This is an all-encompassing question in that it lets the candidate explain at length about their work ethic—thought process, handling QA, working with a team, and managing user feedback. This also lets you dig enough to assess whether the candidate is taking credit for someone else's work or not.

#4 You’ve worked long and hard to deliver a complex feature for a client and they say it’s not what they asked for. How would you take it?

A good developer will take it in their stride, work closely with the client to find the point of disconnect, and sort out the issue. There are so many things that could go wrong or not be to the client’s liking, and it falls on the developer to remain calm and create solutions.

#5 What new programming languages or technologies have you learned recently?

While being certified in many programming languages doesn't guarantee a great developer, it still is an important technical interview question to ask. It helps highlight a thirst for knowledge and shows that the developer is eager to learn new things.

#6 What does the perfect release look like? Who is involved and what is your role?

Have the developer take you through each phase of a recent software development lifecycle. Ask them to explain their specific role in each phase in this release. This will give you an excellent perspective into a developer’s mind. Do they talk about the before and after of the release? A skilled developer would. The chances of something going wrong in a release are very high. How would the developer react? Will they be able to handle the pressure?

SUBSCRIBE to the HackerEarth blog and enrich your monthly reading with our free e-newsletter – Fresh, insightful and awesome articles straight into your inbox from around the tech recruiting world!

#7 Tell me about a time when you had to convince your lead to try a different approach?

As an example of a behavioral interview question, this is a good one. The way a developer approaches this question speaks volumes about how confident they are expressing their views, and how succinct they are in articulating those views.

#8 What have you done with all the extra hours during the pandemic?

Did you binge-watch your way through the pandemic? I’m sure every one of us has done this. Indulge in a lighthearted conversation with your candidate. This lets them talk about something they are comfortable with. Maybe they learned a new skill or took up a hobby. Get to know a candidate’s interests and little pleasures for a more rounded evaluation.

Over to you! Now that you know what aspects of a candidate to focus on, you are well-equipped to bring out the best in each candidate in their interviews. A mix of strong technical skills and interpersonal qualities is how you spot good developers for your team.

If you have more pressing interview questions to add to this list of ours, please write to us at contact@hackerearth.com.

(Part 1) Essential Questions To Ask When Recruiting Developers In 2021

The minute a developer position opens up, recruiters feel a familiar twinge of fear run down their spines. They recall their previous interview experiences, and how there seems to be a blog post a month that goes viral about bad developer interviews.

While hiring managers, especially the picky ones, would attribute this to a shortage of talented developers, what if the time has come to rethink your interview process? What if recruiters and hiring managers put too much stock into bringing out the technical aspects of each candidate and don’t put enough emphasis on their soft skills?

A report by Robert Half shows that 86% of technology leaders say it’s challenging to find IT talent. Interviewing developers should be a rewarding experience, not a challenging one. If you don’t get caught up in asking specific questions and instead design a simple conversation to gauge a candidate’s way of thinking, it throws up a lot of good insight and makes it fun too.

Developer Hiring Statistics

Asking the right technical interview questions when recruiting developers is important but so is clear communication, good work ethic, and alignment with your organization’s goals.

Let us first see what kind of technical interview questions are well-suited to revealing the coding skills and knowledge of any developer, and then tackle the behavioral aspects of the candidate that sets them apart from the rest.

Recruit GREAT developers by asking the right questions

Here are some technical interview questions that you should ask potential software engineers when interviewing.

#1 Write an algorithm for the following

Minimum Stack - Design a stack that provides 4 functions - push(item), pop, peek, and minimum, all in constant order time complexity. Then move on to coding the actual solution.
Kth Largest Element in an array - This is a standard problem with multiple solutions of best time complexity orders where N log(K) is a common one and O(N) + K log(N) is a lesser-known order. Both solutions are acceptable, not directly comparable to each other, and better than N log(N), which is sorting an array and fetching the Kth element.
Top View of a Binary Tree - Given a root node of the binary tree, return the set of all elements that will get wet if it rains on the tree. Nodes having any nodes directly above them will not get wet.
Internal implementation of a hashtable like a map/dictionary - A candidate needs to specify how key-value pairs are stored, hashing is used and collisions are handled. A good developer not only knows how to use this concept but also how it works. If the developer also knows how the data structure scales when the number of records increases in the hashtable, that is a bonus.

Algorithms demonstrate a candidate’s ability to break down a complex problem into steps. Reasoning and pattern recognition capabilities are some more factors to look for when assessing a candidate. A good candidate can code his thought process of the algorithm finalized during the discussion.

Looking for a great place to hire developers in the US? Try Jooble!

#2 Formulate solutions for the below low-level design (LLD) questions

What is LLD? In your own words, specify the different aspects covered in LLD.
Design a movie ticket booking application like BookMyShow. Ensure that your database schema is tailored for a theatre with multiple screens and takes care of booking, seat availability, seat arrangement, and seat locking. Your solution does not have to extend to the payment option.
Design a basic social media application. Design database schema and APIs for a platform like Twitter with features for following a user, tweeting a post, seeing your tweet, and seeing a user's tweet.

Such questions do not have a right or wrong answer. They primarily serve to reveal a developer’s thought process and the way they approach a problem.

Recommended read: Hardest Tech Roles to Fill (+ solutions!)

#3 Some high-level design (HLD) questions

What do you understand by HLD? Can you specify the difference between LLD and HLD?
Design a social media application. In addition to designing a platform like Twitter with features for following a user, tweeting a post, seeing your tweet, and seeing a user's tweet, design a timeline. After designing a timeline where you can see your followers’ tweets, scale it for a larger audience. If you still have time, try to scale it for a celebrity use case.
Design for a train ticket booking application like IRCTC. Incorporate auth, features to choose start and end stations, view available trains and available seats between two stations, save reservation of seats from start to end stations, and lock them till payment confirmation.
How will you design a basic relational database? The database should support tables, columns, basic field types like integer and text, foreign keys, and indexes. The way a developer approaches this question is important. A good developer designs a solution around storage and memory management.

Here’s a pro-tip for you. LLD questions can be answered by both beginners and experienced developers. Mostly, senior developers can be expected to answer HLD questions. Choose your interview questions set wisely, and ask questions relevant to your candidate’s experience.

#4 Have you ever worked with SQL? Write queries for a specific use case that requires multiple joins.

Example: Create a table with separate columns for student name, subject, and marks scored. Return student names and ranks of each student. The rank of a student depends on the total of marks in all subjects.

Not all developers would have experience working with SQL but some knowledge about how data is stored/structured is useful. Developers should be familiar with simple concepts like joins, retrieval queries, and the basics of DBMS.

#5 What do you think is wrong with this code?

Instead of asking developer candidates to write code on a piece of paper (which is outdated, anyway), ask them to debug existing code. This is another way to assess their technical skills. Place surreptitious errors in the code and evaluate their attention to detail.

Now that you know exactly what technical skills to look for and when questions to ask when interviewing developers, the time has come to assess the soft skills of these candidates. Part 2 of this blog throws light on the how and why of evaluating candidates based on their communication skills, work ethic, and alignment with the company’s goals.

View all

Hiring Strategies

Best Pre-Employment Assessments: Optimizing Your Hiring Process for 2024

In today's competitive talent market, attracting and retaining top performers is crucial for any organization's success. However, traditional hiring methods like relying solely on resumes and interviews may not always provide a comprehensive picture of a candidate's skills and potential. This is where pre-employment assessments come into play.

What is Pre-Employement Assessment?

Pre-employment assessments are standardized tests and evaluations administered to candidates before they are hired. These assessments can help you objectively measure a candidate's knowledge, skills, abilities, and personality traits, allowing you to make data-driven hiring decisions.

By exploring and evaluating the best pre-employment assessment tools and tests available, you can:

Improve the accuracy and efficiency of your hiring process.
Identify top talent with the right skills and cultural fit.
Reduce the risk of bad hires.
Enhance the candidate experience by providing a clear and objective evaluation process.

This guide will provide you with valuable insights into the different types of pre-employment assessments available and highlight some of the best tools, to help you optimize your hiring process for 2024.

Why pre-employment assessments are key in hiring

While resumes and interviews offer valuable insights, they can be subjective and susceptible to bias. Pre-employment assessments provide a standardized and objective way to evaluate candidates, offering several key benefits:

Improved decision-making:

By measuring specific skills and knowledge, assessments help you identify candidates who possess the qualifications necessary for the job.
Reduced bias:

Standardized assessments mitigate the risks of unconscious bias that can creep into traditional interview processes.
Increased efficiency:

Assessments can streamline the initial screening process, allowing you to focus on the most promising candidates.
Enhanced candidate experience:

When used effectively, assessments can provide candidates with a clear understanding of the required skills and a fair chance to showcase their abilities.

Types of pre-employment assessments

There are various types of pre-employment assessments available, each catering to different needs and objectives. Here's an overview of some common types:

1. Skill Assessments:

Technical Skills: These assessments evaluate specific technical skills and knowledge relevant to the job role, such as programming languages, software proficiency, or industry-specific expertise. HackerEarth offers a wide range of validated technical skill assessments covering various programming languages, frameworks, and technologies.
Soft Skills: These employment assessments measure non-technical skills like communication, problem-solving, teamwork, and critical thinking, crucial for success in any role.

2. Personality Assessments:

These employment assessments can provide insights into a candidate's personality traits, work style, and cultural fit within your organization.

3. Cognitive Ability Tests:

These tests measure a candidate's general mental abilities, such as reasoning, problem-solving, and learning potential.

4. Integrity Assessments:

These employment assessments aim to identify potential risks associated with a candidate's honesty, work ethic, and compliance with company policies.

By understanding the different types of assessments and their applications, you can choose the ones that best align with your specific hiring needs and ensure you hire the most qualified and suitable candidates for your organization.

Leading employment assessment tools and tests in 2024

Choosing the right pre-employment assessment tool depends on your specific needs and budget. Here's a curated list of some of the top pre-employment assessment tools and tests available in 2024, with brief overviews:

HackerEarth:

A comprehensive platform offering a wide range of validated skill assessments in various programming languages, frameworks, and technologies. It also allows for the creation of custom assessments and integrates seamlessly with various recruitment platforms.
SHL:

Provides a broad selection of assessments, including skill tests, personality assessments, and cognitive ability tests. They offer customizable solutions and cater to various industries.
Pymetrics:

Utilizes gamified assessments to evaluate cognitive skills, personality traits, and cultural fit. They offer a data-driven approach and emphasize candidate experience.
Wonderlic:

Offers a variety of assessments, including the Wonderlic Personnel Test, which measures general cognitive ability. They also provide aptitude and personality assessments.
Harver:

An assessment platform focusing on candidate experience with video interviews, gamified assessments, and skills tests. They offer pre-built assessments and customization options.

Remember: This list is not exhaustive, and further research is crucial to identify the tool that aligns best with your specific needs and budget. Consider factors like the types of assessments offered, pricing models, integrations with your existing HR systems, and user experience when making your decision.

Choosing the right pre-employment assessment tool

Instead of full individual tool reviews, consider focusing on 2–3 key platforms. For each platform, explore:

Target audience: Who are their assessments best suited for (e.g., technical roles, specific industries)?
Types of assessments offered: Briefly list the available assessment categories (e.g., technical skills, soft skills, personality).
Key features: Highlight unique functionalities like gamification, custom assessment creation, or seamless integrations.
Effectiveness: Briefly mention the platform's approach to assessment validation and reliability.
User experience: Consider including user reviews or ratings where available.

Comparative analysis of assessment options

Instead of a comprehensive comparison, consider focusing on specific use cases:

Technical skills assessment:

Compare HackerEarth and Wonderlic based on their technical skill assessment options, focusing on the variety of languages/technologies covered and assessment formats.
Soft skills and personality assessment:

Compare SHL and Pymetrics based on their approaches to evaluating soft skills and personality traits, highlighting any unique features like gamification or data-driven insights.
Candidate experience:

Compare Harver and Wonderlic based on their focus on candidate experience, mentioning features like video interviews or gamified assessments.

Additional tips:

Encourage readers to visit the platforms' official websites for detailed features and pricing information.
Include links to reputable third-party review sites where users share their experiences with various tools.

Best practices for using pre-employment assessment tools

Integrating pre-employment assessments effectively requires careful planning and execution. Here are some best practices to follow:

Define your assessment goals:

Clearly identify what you aim to achieve with assessments. Are you targeting specific skills, personality traits, or cultural fit?
Choose the right assessments:

Select tools that align with your defined goals and the specific requirements of the open position.
Set clear expectations:

Communicate the purpose and format of the assessments to candidates in advance, ensuring transparency and building trust.
Integrate seamlessly:

Ensure your chosen assessment tool integrates smoothly with your existing HR systems and recruitment workflow.
Train your team:

Equip your hiring managers and HR team with the knowledge and skills to interpret assessment results effectively.

Interpreting assessment results accurately

Assessment results offer valuable data points, but interpreting them accurately is crucial for making informed hiring decisions. Here are some key considerations:

Use results as one data point:

Consider assessment results alongside other information, such as resumes, interviews, and references, for a holistic view of the candidate.
Understand score limitations:

Don't solely rely on raw scores. Understand the assessment's validity and reliability and the potential for cultural bias or individual test anxiety.
Look for patterns and trends:

Analyze results across different assessments and identify consistent patterns that align with your desired candidate profile.
Focus on potential, not guarantees:

Assessments indicate potential, not guarantees of success. Use them alongside other evaluation methods to make well-rounded hiring decisions.

Choosing the right pre-employment assessment tools

Selecting the most suitable pre-employment assessment tool requires careful consideration of your organization's specific needs. Here are some key factors to guide your decision:

Industry and role requirements:

Different industries and roles demand varying skill sets and qualities. Choose assessments that target the specific skills and knowledge relevant to your open positions.
Company culture and values:

Align your assessments with your company culture and values. For example, if collaboration is crucial, look for assessments that evaluate teamwork and communication skills.
Candidate experience:

Prioritize tools that provide a positive and smooth experience for candidates. This can enhance your employer brand and attract top talent.

Budget and accessibility considerations

Budget and accessibility are essential factors when choosing pre-employment assessments:

Budget:

Assessment tools come with varying pricing models (subscriptions, pay-per-use, etc.). Choose a tool that aligns with your budget and offers the functionalities you need.
Accessibility:

Ensure the chosen assessment is accessible to all candidates, considering factors like language options, disability accommodations, and internet access requirements.

Additional Tips:

Free trials and demos: Utilize free trials or demos offered by assessment platforms to experience their functionalities firsthand.
Consult with HR professionals: Seek guidance from HR professionals or recruitment specialists with expertise in pre-employment assessments.
Read user reviews and comparisons: Gain insights from other employers who use various assessment tools.

By carefully considering these factors, you can select the pre-employment assessment tool that best aligns with your organizational needs, budget, and commitment to an inclusive hiring process.

Remember, pre-employment assessments are valuable tools, but they should not be the sole factor in your hiring decisions. Use them alongside other evaluation methods and prioritize building a fair and inclusive hiring process that attracts and retains top talent.

Future trends in pre-employment assessments

The pre-employment assessment landscape is constantly evolving, with innovative technologies and practices emerging. Here are some potential future trends to watch:

Artificial intelligence (AI):

AI-powered assessments can analyze candidate responses, written work, and even resumes, using natural language processing to extract relevant insights and identify potential candidates.
Adaptive testing:

These assessments adjust the difficulty level of questions based on the candidate's performance, providing a more efficient and personalized evaluation.
Micro-assessments:

Short, focused assessments delivered through mobile devices can assess specific skills or knowledge on-the-go, streamlining the screening process.
Gamification:

Engaging and interactive game-based elements can make the assessment experience more engaging and assess skills in a realistic and dynamic way.

Conclusion

Pre-employment assessments, when used thoughtfully and ethically, can be a powerful tool to optimize your hiring process, identify top talent, and build a successful workforce for your organization. By understanding the different types of assessments available, exploring top-rated tools like HackerEarth, and staying informed about emerging trends, you can make informed decisions that enhance your ability to attract, evaluate, and hire the best candidates for the future.

Hiring Strategies

Tech Layoffs: What To Expect In 2024

Layoffs in the IT industry are becoming more widespread as companies fight to remain competitive in a fast-changing market; many turn to layoffs as a cost-cutting measure. Last year, 1,000 companies including big tech giants and startups, laid off over two lakhs of employees. But first, what are layoffs in the tech business, and how do they impact the industry?

Tech layoffs are the termination of employment for some employees by a technology company. It might happen for various reasons, including financial challenges, market conditions, firm reorganization, or the after-effects of a pandemic. While layoffs are not unique to the IT industry, they are becoming more common as companies look for methods to cut costs while remaining competitive.

The consequences of layoffs in technology may be catastrophic for employees who lose their jobs and the firms forced to make these difficult decisions. Layoffs can result in the loss of skill and expertise and a drop in employee morale and productivity. However, they may be required for businesses to stay afloat in a fast-changing market.

This article will examine the reasons for layoffs in the technology industry, their influence on the industry, and what may be done to reduce their negative impacts. We will also look at the various methods for tracking tech layoffs.

What are tech layoffs?

The term "tech layoff" describes the termination of employees by an organization in the technology industry. A company might do this as part of a restructuring during hard economic times.

In recent times, the tech industry has witnessed a wave of significant layoffs, affecting some of the world’s leading technology companies, including Amazon, Microsoft, Meta (formerly Facebook), Apple, Cisco, SAP, and Sony. These layoffs are a reflection of the broader economic challenges and market adjustments facing the sector, including factors like slowing revenue growth, global economic uncertainties, and the need to streamline operations for efficiency.

Each of these tech giants has announced job cuts for various reasons, though common themes include restructuring efforts to stay competitive and agile, responding to over-hiring during the pandemic when demand for tech services surged, and preparing for a potentially tough economic climate ahead. Despite their dominant positions in the market, these companies are not immune to the economic cycles and technological shifts that influence operational and strategic decisions, including workforce adjustments.

This trend of layoffs in the tech industry underscores the volatile nature of the tech sector, which is often at the mercy of rapid changes in technology, consumer preferences, and the global economy. It also highlights the importance of adaptability and resilience for companies and employees alike in navigating the uncertainties of the tech landscape.

Causes for layoffs in the tech industry

Why are tech employees suffering so much?

Yes, the market is always uncertain, but why resort to tech layoffs?

Various factors cause tech layoffs, including company strategy changes, market shifts, or financial difficulties. Companies may lay off employees if they need help to generate revenue, shift their focus to new products or services, or automate certain jobs.

In addition, some common reasons could be:

Financial struggles

Currently, the state of the global market is uncertain due to economic recession, ongoing war, and other related phenomena. If a company is experiencing financial difficulties, only sticking to pay cuts may not be helpful—it may need to reduce its workforce to cut costs.

Also, read: 6 Steps To Create A Detailed Recruiting Budget (Template Included)

Changes in demand

The tech industry is constantly evolving, and companies would have to adjust their workforce to meet changing market conditions. For instance, companies are adopting remote work culture, which surely affects on-premises activity, and companies could do away with some number of tech employees at the backend.

Restructuring

Companies may also lay off employees as part of a greater restructuring effort, such as spinning off a division or consolidating operations.

Automation

With the advancement in technology and automation, some jobs previously done by human labor may be replaced by machines, resulting in layoffs.

Mergers and acquisitions

When two companies merge, there is often overlap in their operations, leading to layoffs as the new company looks to streamline its workforce.

But it's worth noting that layoffs are not exclusive to the tech industry and can happen in any industry due to uncertainty in the market.

Will layoffs increase in 2024?

It is challenging to estimate the rise or fall of layoffs. The overall state of the economy, the health of certain industries, and the performance of individual companies will play a role in deciding the degree of layoffs in any given year.

But it is also seen that, in the first 15 days of this year, 91 organizations laid off over 24,000 tech workers, and over 1,000 corporations cut down more than 150,000 workers in 2022, according to an Economic Times article.

The COVID-19 pandemic caused a huge economic slowdown and forced several businesses to downsize their employees. However, some businesses rehired or expanded their personnel when the world began to recover.

So, given the current level of economic uncertainty, predicting how the situation will unfold is difficult.

Also, read: 4 Images That Show What Developers Think Of Layoffs In Tech

What types of companies are prone to tech layoffs?

Tech layoffs can occur in organizations of all sizes and various areas.

Following are some examples of companies that have experienced tech layoffs in the past:

Large tech firms

Companies such as IBM, Microsoft, Twitter, Better.com, Alibaba, and HP have all experienced layoffs in recent years as part of restructuring initiatives or cost-cutting measures.

Market scenarios are still being determined after Elon Musk's decision to lay off employees. Along with tech giants, some smaller companies and startups have also been affected by layoffs.

Startups

Because they frequently work with limited resources, startups may be forced to lay off staff if they cannot get further funding or need to pivot due to market downfall.

Small and medium-sized businesses

Small and medium-sized businesses face layoffs due to high competition or if the products/services they offer are no longer in demand.

Companies in certain industries

Some sectors of the technological industry, such as the semiconductor industry or automotive industry, may be more prone to layoffs than others.

Companies that lean on government funding

Companies that rely significantly on government contracts may face layoffs if the government cuts technology spending or contracts are not renewed.

How to track tech layoffs?

You can’t stop tech company layoffs, but you should be keeping track of them. We, HR professionals and recruiters, can also lend a helping hand in these tough times by circulating “layoff lists” across social media sites like LinkedIn and Twitter to help people land jobs quicker. Firefish Software put together a master list of sources to find fresh talent during the layoff period.

Because not all layoffs are publicly disclosed, tracking tech industry layoffs can be challenging, and some may go undetected. There are several ways to keep track of tech industry layoffs:

Use tech layoffs tracker

Layoff trackers like thelayoff.com and layoffs.fyi provide up-to-date information on layoffs.

In addition, they aid in identifying trends in layoffs within the tech industry. It can reveal which industries are seeing the most layoffs and which companies are the most affected.

Companies can use layoff trackers as an early warning system and compare their performance to that of other companies in their field.

News articles

Because many news sites cover tech layoffs as they happen, keeping a watch on technology sector stories can provide insight into which organizations are laying off employees and how many individuals have been affected.

Social media

Organizations and employees frequently publish information about layoffs in tech on social media platforms; thus, monitoring companies' social media accounts or following key hashtags can provide real-time updates regarding layoffs.

Online forums and communities

There are online forums and communities dedicated to discussing tech industry news, and they can be an excellent source of layoff information.

Government reports

Government agencies such as the Bureau of Labor Statistics (BLS) publish data on layoffs and unemployment, which can provide a more comprehensive picture of the technology industry's status.

How do companies reduce tech layoffs?

Layoffs in tech are hard – for the employee who is losing their job, the recruiter or HR professional who is tasked with informing them, and the company itself. So, how can we aim to avoid layoffs? Here are some ways to minimize resorting to letting people go:

Salary reductions

Instead of laying off employees, businesses can lower the salaries or wages of all employees. It can be accomplished by instituting compensation cuts or salary freezes.

Implementing a hiring freeze

Businesses can halt employing new personnel to cut costs. It can be a short-term solution until the company's financial situation improves.

Also, read: What Recruiters Can Focus On During A Tech Hiring Freeze

Non-essential expense reduction

Businesses might search for ways to cut or remove non-essential expenses such as travel, training, and office expenses.

Reducing working hours

Companies can reduce employee working hours to save money, such as implementing a four-day workweek or a shorter workday.

These options may not always be viable and may have their problems, but before laying off, a company owes it to its people to consider every other alternative, and formulate the best solution.

Tech layoffs to bleed into this year

While we do not know whether this trend will continue or subside during 2023, we do know one thing. We have to be prepared for a wave of layoffs that is still yet to hit. As of last month, Layoffs.fyi had already tracked 170+ companies conducting 55,970 layoffs in 2023.

So recruiters, let’s join arms, distribute those layoff lists like there’s no tomorrow, and help all those in need of a job! :)

Hiring Tools

What is Headhunting In Recruitment?: Types & How Does It Work?

In today’s fast-paced world, recruiting talent has become increasingly complicated. Technological advancements, high workforce expectations and a highly competitive market have pushed recruitment agencies to adopt innovative strategies for recruiting various types of talent. This article aims to explore one such recruitment strategy – headhunting.

What is Headhunting in recruitment?

In headhunting, companies or recruitment agencies identify, engage and hire highly skilled professionals to fill top positions in the respective companies. It is different from the traditional process in which candidates looking for job opportunities approach companies or recruitment agencies. In headhunting, executive headhunters, as recruiters are referred to, approach prospective candidates with the hiring company’s requirements and wait for them to respond. Executive headhunters generally look for passive candidates, those who work at crucial positions and are not on the lookout for new work opportunities. Besides, executive headhunters focus on filling critical, senior-level positions indispensable to companies. Depending on the nature of the operation, headhunting has three types. They are described later in this article. Before we move on to understand the types of headhunting, here is how the traditional recruitment process and headhunting are different.

How do headhunting and traditional recruitment differ from each other?

Headhunting is a type of recruitment process in which top-level managers and executives in similar positions are hired. Since these professionals are not on the lookout for jobs, headhunters have to thoroughly understand the hiring companies’ requirements and study the work profiles of potential candidates before creating a list.

In the traditional approach, there is a long list of candidates applying for jobs online and offline. Candidates approach recruiters for jobs. Apart from this primary difference, there are other factors that define the difference between these two schools of recruitment.

AspectHeadhuntingTraditional RecruitmentCandidate TypePrimarily passive candidateActive job seekersApproachFocused on specific high-level rolesBroader; includes various levelsScopeproactive outreachReactive: candidates applyCostGenerally more expensive due to expertise requiredTypically lower costsControlManaged by headhuntersManaged internally by HR teams

All the above parameters will help you to understand how headhunting differs from traditional recruitment methods, better.

Types of headhunting in recruitment

Direct headhunting: In direct recruitment, hiring teams reach out to potential candidates through personal communication. Companies conduct direct headhunting in-house, without outsourcing the process to hiring recruitment agencies. Very few businesses conduct this type of recruitment for top jobs as it involves extensive screening across networks outside the company’s expanse.

Indirect headhunting: This method involves recruiters getting in touch with their prospective candidates through indirect modes of communication such as email and phone calls. Indirect headhunting is less intrusive and allows candidates to respond at their convenience.Third-party recruitment: Companies approach external recruitment agencies or executive headhunters to recruit highly skilled professionals for top positions. This method often leverages the company’s extensive contact network and expertise in niche industries.

How does headhunting work?

Finding highly skilled professionals to fill critical positions can be tricky if there is no system for it. Expert executive headhunters employ recruitment software to conduct headhunting efficiently as it facilitates a seamless recruitment process for executive headhunters. Most software is AI-powered and expedites processes like candidate sourcing, interactions with prospective professionals and upkeep of communication history. This makes the process of executive search in recruitment a little bit easier. Apart from using software to recruit executives, here are the various stages of finding high-calibre executives through headhunting.

Identifying the role

Once there is a vacancy for a top job, one of the top executives like a CEO, director or the head of the company, reach out to the concerned personnel with their requirements. Depending on how large a company is, they may choose to headhunt with the help of an external recruiting agency or conduct it in-house. Generally, the task is assigned to external recruitment agencies specializing in headhunting. Executive headhunters possess a database of highly qualified professionals who work in crucial positions in some of the best companies. This makes them the top choice of conglomerates looking to hire some of the best talents in the industry.

Defining the job

Once an executive headhunter or a recruiting agency is finalized, companies conduct meetings to discuss the nature of the role, how the company works, the management hierarchy among other important aspects of the job. Headhunters are expected to understand these points thoroughly and establish a clear understanding of their expectations and goals.

Candidate identification and sourcing

Headhunters analyse and understand the requirements of their clients and begin creating a pool of suitable candidates from their database. The professionals are shortlisted after conducting extensive research of job profiles, number of years of industry experience, professional networks and online platforms.

Approaching candidates

Once the potential candidates have been identified and shortlisted, headhunters move on to get in touch with them discreetly through various communication channels. As such candidates are already working at top level positions at other companies, executive headhunters have to be low-key while doing so.

Assessment and Evaluation

In this next step, extensive screening and evaluation of candidates is conducted to determine their suitability for the advertised position.

Interviews and negotiations

Compensation is a major topic of discussion among recruiters and prospective candidates. A lot of deliberation and negotiation goes on between the hiring organization and the selected executives which is facilitated by the headhunters.

Finalizing the hire

Things come to a close once the suitable candidates accept the job offer. On accepting the offer letter, headhunters help finalize the hiring process to ensure a smooth transition.

The steps listed above form the blueprint for a typical headhunting process. Headhunting has been crucial in helping companies hire the right people for crucial positions that come with great responsibility. However, all systems have a set of challenges no matter how perfect their working algorithm is. Here are a few challenges that talent acquisition agencies face while headhunting.

Common challenges in headhunting

Despite its advantages, headhunting also presents certain challenges:

Cost Implications: Engaging headhunters can be more expensive than traditional recruitment methods due to their specialized skills and services.

Time-Consuming Process: While headhunting can be efficient, finding the right candidate for senior positions may still take time due to thorough evaluation processes.

Market Competition: The competition for top talent is fierce; organizations must present compelling offers to attract passive candidates away from their current roles.

Although the above mentioned factors can pose challenges in the headhunting process, there are more upsides than there are downsides to it. Here is how headhunting has helped revolutionize the recruitment of high-profile candidates.

Advantages of Headhunting

Headhunting offers several advantages over traditional recruitment methods:

Access to Passive Candidates: By targeting individuals who are not actively seeking new employment, organisations can access a broader pool of highly skilled professionals.

Confidentiality: The discreet nature of headhunting protects both candidates’ current employment situations and the hiring organisation’s strategic interests.

Customized Search: Headhunters tailor their search based on the specific needs of the organization, ensuring a better fit between candidates and company culture.

Industry Expertise: Many headhunters specialise in particular sectors, providing valuable insights into market dynamics and candidate qualifications.

Conclusion

Although headhunting can be costly and time-consuming, it is one of the most effective ways of finding good candidates for top jobs. Executive headhunters face several challenges maintaining the g discreetness while getting in touch with prospective clients. As organizations navigate increasingly competitive markets, understanding the nuances of headhunting becomes vital for effective recruitment strategies. To keep up with the technological advancements, it is better to optimise your hiring process by employing online recruitment software like HackerEarth, which enables companies to conduct multiple interviews and evaluation tests online, thus improving candidate experience. By collaborating with skilled headhunters who possess industry expertise and insights into market trends, companies can enhance their chances of securing high-caliber professionals who drive success in their respective fields.

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What is Headhunting In Recruitment?: Types & How Does It Work?