How to hire Data Scientists?

Hiring a great set of data scientists can be challenging. From sourcing suitable candidates to evaluating for the right skills, finding the perfect person could take months, if not done right. is a DPhi product – a community of AI and technology enthusiasts from 140+ countries. We have helped a range of businesses, starting from seed-stage startups to matured businesses in data science hiring. In this blog, we’re listing a few key elements that can help you hire top-notch data scientists at your company.


What’s the issue with data science hiring?

Generally, it’s said that poorly crafted job descriptions restrict companies from getting viable candidates for data science hiring. However, the problem extends beyond this. Companies may or may not struggle to get applicants for data science positions. The challenge remains with attracting and identifying the right people among the noisy hiring funnel. It has become increasingly difficult for recruiters to identify the best data scientists purely based on their social profiles or resumes. This can also be attributed to the surge in the number of available AI/ML certifications and ambiguity in the job titles. In this blog, we cover a few essentials of hiring data scientists at your company that can help you overcome some of these challenges.


1. Define data scientists as per your data science or technology team.

Want to hire the best data scientists? You should first decide what they’re expected to do. Often, hiring teams confuse roles such as data analysts, data scientists, data engineers, and others. We wouldn’t delve into these definitions because there is enough material out there (we’ve cited a few of them towards the end), and your engineering or data science leaders would probably have clarity about each of these roles. For you, it’s essential to ensure that you jot down the attributes you want in your first or next set of data science hires.

These are some things you should think about –

  • Tasks and responsibilities of the data scientist.
  • Skills are needed to perform those tasks and responsibilities.
  • Goals or challenges the new data scientists will face, and the qualities necessary to achieve them.
  • Type of skillset best complements your team’s existing abilities.
  • You need a Generalist or Specialist? Is a specific experience in a particular tech stack or research area necessary? 

You should reflect on these aspects until you understand the type of data scientist you need to succeed in your role. Once you have determined them, it’s time to create a compelling job description.


2. Write a Job Description that Sells

The job description should provide three things: a general overview, an explanation of responsibilities, and a list of experience and education requirements. The formula may seem simple, but writing an effective job description is where most of the data science recruiters fail as per this Techcrunch article. You must market the role in the description to attract as many applications as possible.

In the same vein, you need to ensure that your job description attracts the talent with the right level of technical expertise. In the field of data science, teams hire both hire Ph.D. or seasoned post-doctoral researchers and fresh undergraduate graduates. Depending on the level of proficiency your engineer team needs, you should draft the job description.


3. Hire the right talent by posting your jobs where data scientists will find them 

To receive the best applications, where should you post your opportunity? One answer is traditional job sites like LinkedIn or Angel List. In most cases, you would get quite a few applicants from each of these platforms if your job description is aptly drafted. You can, however, promote your opportunity in more effective ways. If you have posted your job openings on the right platforms, you can utilize an outbound strategy, where your team contacts ideal candidates on those platforms.

When doing so, it would be helpful to leverage specific indicators for a fit. For example, you can try to reach out to individuals who have completed DPhi’s extensive deep learning bootcamp and have earned a certification for it. Using Github, you can explore profiles and their repositories to target a specific group of people. Or, if you’re hiring experienced data scientists, it could be helpful to look for data scientists with relevant projects or similar work experience at companies in similar domains to complete the process optimally.


4. Set up a data science hiring assessment and invite candidates

One of the biggest challenges that data science hiring teams face is excessive noise in the hiring pipeline. It can be caused by a variety of factors, and it goes beyond the scope of your job description. Therefore it’s very important to leverage a powerful data science assessment tool. It will provide critical functionality such as AI/ML challenges on real-world datasets, algorithmic questions, and multiple-choice questions for you to make sure you are covering the various skill sets required in your next data science hires.

Candidate attempting a data science hiring test on


The right assessment platform improves the candidate experience and gives the data and problems that reflect the real challenges that you expect data scientists to solve.

Using such a platform for data science hiring can substantially optimize your processes. It’ll reduce the time your engineers spend interviewing candidates by helping them to interview only those candidates who demonstrate hands-on competency during the data science assessments. Furthermore, you would be able to test the maximum number of candidates and use the high volume of applicants to your advantage.


Want to know more about the best practices that you can adopt for hiring data scientists?

Looking to hire data scientists or analytics professionals at scale or want insights on best practices? You can request a demo with our team to understand how has solved these problems for various businesses.


A few additional links for your reference –

  • Towards Data Science publication on ten different data science job titles and their meanings – here.
  • How Airbnb’s analytics team leveraged data challenges in their process – here.
  • Forbes article on how to hire your first data scientist – here.


Set up your free data science hiring test

On, you get a 14-day free trial. On signing up, you can evaluate up to 50 candidates in your data science hiring pipeline. You can sign up for the platform here.