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How to hire a data scientist in 2022: A complete guide

Conduct a successful data scientist recruitment process in 2022

Table of Contents

  • Why hire a data scientist?
  • Different roles and specializations of a data scientist
  • How to source data scientists?
  • How to assess data scientists?
  • Interview questions for data scientists
  • Core competencies for a data scientist
  • Hiring entry-level Vs senior-level data scientists

Data Scientists are some of the most in-demand professionals of the 21st century. Companies across industries are on the lookout for professionals who can deal with their data banks to make effective business decisions. Domestic companies, MNCs, captive centers as well as AI/ Analytics based startups are all looking to unlock the untapped talent pool of data science and data analytics professionals in India. 

According to a recent study by Analytics India Mag, there has been a 73.5% increase in open data science and analytics jobs from the start of the pandemic to April 2022. Because of the growing demand and the impact they can have on business processes, it has become increasingly difficult to hire skilled talent in Data Science.

At Xpheno, we provide specialist staffing solutions to companies across industries. My experience in successfully deploying tech talent as per the hiring demands of our clientele across IT services and products companies, GICs, and funded tech startups has helped me identify what goes into hiring competent, suitable candidates. In this article, I am going to share how organizations can identify skilled data scientists and retain them in the current competitive marketplace.

Why hire a data scientist?

The onset of digitization and IoT has made data the most important asset in the 21st-century business world. Businesses store information in digital systems through IoT platforms and process it with analytics software and artificial intelligence. It is helping enterprises deliver better results. That is why many of our clients and companies across industries are now actively looking for competent candidates to fill the data scientist post in the organization.

Here’s what a data scientist can do for your organization: 

We know that data scientists analyze data which allows them to make effective decisions in building creative business strategies.  

Here’s how they do it:

  • Help the business predict consumer behavior by studying existing patterns in the data. 
  • Use machine learning to build predictive models to make customer support interactions proactive and largely automatic.

This makes the data scientist important in a variety of fields, from finance to pharmaceuticals, from manufacturing to retail. Companies know that hiring experts in future skills and technologies is the best way to get a competitive advantage in the current competitive market landscape. Data scientists have thus become an essential part of the future-proof workforce.

However, before conducting the data scientist recruitment process, companies must establish their objective for hiring data science professionals and define their hiring parameters accordingly. Depending on the industry and their specialization, data scientists can take up different roles in an organization:

Different roles and specializations of a Data Scientist

Quality Analyst

The quality analyst uses the power of data analysis to improve performance by making the planning and implementation efficient and faster. They further conduct testing and regular audits to make sure the product meets company standards and deadlines. Typically hired by logistics companies, quality analysts deploy tools to measure and improve the efficiency of manufacturing and production processes. For example, they can improve the assembly lines or optimize the supply chain to make transportation efficient.

Business Analysts

Business Analysts are data scientists who analyze data banks to assess existing business models and enhance business processes. They do this by recommending developments and changes in plans backed by data analysis and data visualization. This results in an improvement in the return on investment.

Actuarial Analysts

Actuarial scientists are data scientists employed by the banking, insurance, and investment sector. They assess risks involved in these financial processes for companies. Using machine learning and complex mathematical algorithms, they build statistical models that can help analyze stocks and make predictions of premiums and losses from other financial investments.

Programmer analyst

Programmer analysts use programming and data analysis to write and optimize algorithms to develop and insert computer programs and software. They have the ability to research, design, develop and test software applications and systems that automate business processes. That is why various companies hire programmer analysts to develop company-specific programs and systems that can help maximize the efficiency of their business operations. The nature of work makes them fit for different industries like software, finance, and manufacturing.

Spatial data scientists

Spacial data scientists deal with geo-spacial data by developing geographical information systems (GIS) to map and analyze data. They can work on improving GPS (Global Positioning Devices) to help businesses, the military, and various other organizations with useful information like spacial predictions and correlations between events. For this, they deploy mathematical algorithms to model complex phenomena.

No matter the domain they work on, data scientists can create an impact with their insights. So let’s look at where you can source data scientists

How to source data scientists?

Although the ongoing talent crunch makes it harder to find and attract candidates, we are now using online tech communities to discover and source tech talent. Git Hub, Stack Overflow, HackerEarth, and Kaggle are some of the platforms recruiters have been using to shortlist data science profiles.

For this, they

  • Pay attention to their work in repositories like Github and interactions on Stack overflow and other platforms to understand their work and their preferred domain. 
  • Cross-check their profile on Linkedin, Twitter, or Reddit to make sure they are the right fit.

Several companies also conduct Hackathons and coding challenges to attract and assess candidates at the same time.

However, with top companies hiring data scientists and offering sky-high packages, small and medium enterprises have to go a step further to attract the right candidates. Workplace culture, career progression, and autonomy become attractive attributes that make an organization a great place to work for data scientists. Therefore, companies must optimize their hiring funnel by paying close attention to candidate touch points, from job descriptions to follow-up emails. Make sure that brand communication across all channels, from social media pages to cold calls, is finetuned to draw candidates.

How to assess data scientists for their skills?

As mentioned above, all data scientists deal with data, but they might specialize in certain skillsets. That is why, when evaluating data scientists for skills, it is vital to understand the need of the particular enterprise. This would help determine which skills the recruiters should prefer in the interview. 

There are some prerequisites such as proficiency in data mining, statistical analysis, a programming language, data structures and algorithms, libraries, SQL, tableau, and predictive modeling.

But along with this, they should also possess a good understanding of the business domain/ industry that the company belongs to. So that they can understand the business problems and provide effective solutions through data science.

Technical skill assessment test

Recruiting skilled data scientists becomes a challenge if the candidates are not assessed for the skills mentioned in their resumes. A technical skill assessment on a coding test platform is a good way to select candidates for the interview round. Platforms like Hackerrearth, Machine Hack Assessment, Data camp signal, and the test gorilla data science test are platforms that evaluate candidates by providing them with real-world machine learning problems. Whether hiring for an entry-level or a senior-level position, these platforms test their algorithmic intuition by giving them large, complex data sets to break down within a stipulated time. 

Interview questions for data scientists

A well-done interview would allow you to assess the technical as well as interpersonal skills of a data scientist. The best way to do that is to look through their project portfolio and base the questions around the projects.

  • What impact does their project create in the environment: whether it solves a particular problem, makes a process efficient, or improves customer/user experience. 
  • Their thought process behind building their model. 
  • Problems they encountered along the way and how they overcame those challenges.

Asking them to explain their project would not only provide a window to their technical know-how but also their problem-solving skills as well as their ability to communicate.

Core competencies of a Data Scientist

Data scientist is a role that closely follows industry developments and uses new age technologies to ease business processes. Several companies are hyperfocused on finding professionals who keep up with new technologies and have AI and ML skill sets. But there are also some non-technical skills that might get overlooked. 

Sure, they must have knowledge of statistics, business intelligence, machine learning, data architecture, and data analytics. However, to make a high-functioning and impactful team with data science professionals, a lot more has to be considered. Other than the technical skills, there are some core competencies that are expected from the data science and data analytics role. Let us list those important competencies for a data scientist


As mentioned earlier, a data scientist builds statistical models and recommends changes in the existing processes to make them efficient. That is why it’s a high-stakes role that involves communication with key stakeholders of an organization. Communicating the value of their insights to the top decision-makers becomes very important.


Data scientist is a multifaceted role, it requires a team player with collaboration and interpersonal skills, along with analytical skills. The person should be comfortable working in dynamic teams with technical and non-technical professionals on multiple ongoing projects.

Business understanding

Data scientist is a role that requires a good understanding of the business processes to suggest improvements. The precious information becomes useless if it isn’t used effectively to make business decisions. Therefore, having basic business acumen and keeping up with the current and upcoming industry trends and the market is a must.  

Data Intuition

Data intuition comes from experience. Working with complex algorithms and statistical data provides an intuitive understanding of concepts. Data without intuition can lead to misleading results. That is why to do an objective analysis and uncover hidden and overlooked insights, data scientists should have pattern recognition ability, also called data intuition. 

Motivated and Proactive

Moreover, like in any other field, they should be motivated and self-driven. Having a curious mindset and an ability to take initiative opens up domains for problem-solving. 

Hiring entry-level Vs senior-level data scientists

Another question that puzzles the recruiters is whether to hire entry-level or senior level data scientists? Although it depends on the business needs of the industry, most companies prefer to hire experienced data scientists with more than 5 years of working in the industry. However, in the current competitive market, data scientists with even little industry experience can prove to be a great addition to the organization. 

As said before, the role of a data scientist demands a lot more than merely technical acumen. A fresher might have some core competencies that an experienced data scientist might also lack. Therefore, a fresher with an impressive portfolio, who keeps up to date with the constantly evolving market and technologies can prove to be a competent decision-maker in the organization. Although 98.6% of companies hire full-time data science professionals in India, hiring freelance data scientists is also an option for a one-time project, if the company is not yet sure about the long-term goals of a data scientist in the organization. Therefore, depending on the size and the requirements of the organization, you can define and campaign for the data scientist vacancies. 

The bottom line

Data scientist is a role that can unlock great business potential for a company with their insights. That is why it is crucial to have a well-defined hiring strategy that can open up a talent pool skilled in data science. Our happy clients prove that when companies provide the time and space for data science professionals to figure out creative solutions to complex business problems and provide the scope for career progression, they can attract and retain data science professionals as great assets for the organization. Xpheno is a specialist staffing firm that helps companies meet skilled talent. Reach out to us to find data scientists who could be the right fit for your organization. 


Written by,
Sundar Eshwar – IT Staffing

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