How to Hire the Ideal Analytics Professional?

Today, the booming data economy and AI-first mindset among businesses have led to a proliferation of data and analytics jobs in the industry. Professionals are cued in on the huge demand for analytics in the job market. Bain & Company estimates, “By 2020, the worldwide advanced analytics talent pool is expected to reach 1 million people, double 2018 levels.”[1] However, there’s also a growing gap between roles that analytics professionals are expected to fill and the hands-on abilities of candidates. Let’s take a look at how organizations can address inadequacies in the job market and hire the ideal analytics professional.


Data scientists usually have an overarching skillset that covers mathematics, software/ coding and/or domain expertise. It’s not every day that you come across a data scientist who is proficient in all three disciplines. In fact, many professionals check these three boxes only after years of experience in the industry. The rare few who have a strong grasp of all three subject matters are often branded as “unicorns”. While it’s good to have a unicorn on your team, they’re hard to come by. And, you do not always need a unicorn. Ideally, you want your hiring focus to be data skillsets that match your business analytics goals. For instance, a professional who has a strong grasp of programming languages and mathematics, is probably a good fit for projects that involve developing software frameworks, algorithms, or productizing business insight frameworks. On the other hand, if the analytics efforts are pertinent to industries such as insurance or healthcare, then it helps to hire a professional with strong domain expertise (industry trends, guidelines, practices etc.) and ML knowhow. The pace at which the data analytics industry is evolving, naturally calls for professionals who are excited about learning, and picking pick up the latest coding language, tools and frameworks. It pays to have professionals with a learning mindset on your team.

Many organizations approach the job of hiring an analyst as tallying a checklist to gauge the candidate’s proficiency in R, Python, Hadoop, SQL, Spark, statistical analysis, machine learning etc. While these are essential parts of the skillset, you cannot undermine the need for soft skills in data scientists and analysts. What good is data and analysis, if a professional cannot understand its relevance in business context? Look for data analysts who have the right aptitude to crunch numbers and uncover crucial insights in the purview of business problems. Ones who are good storytellers and can break down analytics jargon to everyday business language and present key insights to C-suite members. Analytics consumption is mostly driven top-down, and it takes a good storyteller to show boardroom members the value that analytics can bring in.


Given the shortage of data scientists and analysts, recruiters are in a flurry of confusion, on whether they should hire the next analytics professional that they find in the job market or fresh-out-of-university grads to close the demand-supply gap. The answer to that of course varies, as you can always have a novice data scientist with a great aptitude and promise of going far in the industry – the only sure-fire way of knowing would be to test their skills and mettle. It’s also a commonplace for businesses to find a disparity between what analytics professionals say they know and how much of it can actually be applied in business context. Avoiding these say-do gaps begins with screening candidates more efficiently. Do not make the mistake of being swayed solely by CVs that speak of proficiency in data science and analytics languages; make sure that your candidates truly have a data and analytics mindset. Know that a strong analytics mindset and aptitude trumps theoretical knowhow.

It’s safe to say that analytics professionals are cognizant of the shortage in the talent pool, the huge influx of data analyst jobs, and the lucrative job offers and salary hikes doing rounds in the industry, as a result. Many businesses make the mistake of assuming that a plum well-paying job, can do the job and rope in talent. But then it becomes a question of how much more can you pay than the next employer? When ideally, you should be asking yourself – how can you retain your data science talent with more than just a paycheck. Think on the lines of offering training opportunities where data scientists can upskill themselves and diversify their expertise, provide a clear roadmap for career growth, serve up a great culture – now there’s an offer they cannot refuse.


“Train people well enough so they can leave, treat them well enough so they don’t want to.”


 We have handled large hiring volumes in the past, and helped many businesses set up centres of excellence with dedicated in-house data and analytics teams skilled in their respective domains. Below, we have outlined a general set of guidelines, drawing from our recruitment approach, to help you streamline your hiring strategy:

 Do you have the right hiring panel?

-It takes a hiring panel with a broad skillset to be able to test candidates adequately

-A well-rounded interviewing panel includes experts in data analysis, statistics, machine learning, programming, big data, BI and visualization etc.

How do you test if candidates are hands-on?

-Situation-based case studies can shed light on how candidates approach a problem

-Take-home tests and short exercises tests that gauge a candidate’s ability to solve a series of increasingly difficult challenges

-Capstone project statements and Hackathons can be a part of campus recruitment drives

-Grit test can help test if candidates are go-getters and understand how they cope with pressure

Do candidates understand what it will be like to work in your organization?

-Create a process where you give candidates data and problems that reflect the real challenges that they will face in your organization

-Ensure that your hiring process offers candidates an opportunity to engage with the team’s dynamics and culture so that they get a taste of what it will be like to work with you

-Each candidate should complete the interview process with a trusting sense of what it would be like to join your team

What other channels can be used for interviewing purpose?

-Many organizations are moving to video resumes to screen candidates

-While the traditional approach of face-to-face interviews work the best, given that people are spread across locations, organizations are growing flexible and adopting various interview modes

-Organizations should make the best use of platforms tailored to conduct video interviews, test technical coding skills, etc. while interviewing candidates in remote locations

What are some key considerations while scaling up the team?

-Gain clarity on business hiring requirements by assessing the analytical maturity of your organization and identifying existing gaps while defining job roles. Map out competency profiles that outline the primary and secondary skillsets expected in candidates

-Enhance brand visibility and marketing efforts to engage and reach out to the talent pool. Brand your business well internally, so candidates get a taste of your culture and are excited about joining your organization. Make sure that you have a defined career path, training modules, benefits, etc. to give candidates more reasons to join your organization

-The data analytics network is a close-knit community. Getting your foot in the door and fraternizing with experienced contacts are essential parts of connecting with the right candidates who can fill these job roles. Also leverage referral networks, third party vendors and campus recruitment drives to find the right talent

-Make sure the talent pool does not get diluted while hiring at scale. Set the right processes in place from the start, so your hiring system does not crack under the pressure of large hiring volumes. Interviewing processes should address technical, domain, situational and psychometric aspects, so your hiring funnel is clean and screens the right talent, regardless of the hiring volume. Here is a snapshot of our proven interview approach to adeptly gauge candidates holistically, on skillsets and persona:

The sexiest job of the 21st century has and is naturally going to attract a fair share of interest from professionals pursuing data science and analytics careers primarily for the buzz that it is creating in the industry. And then you have professionals who truly enjoy crunching numbers, sifting through data, and gleaning useful insights – now there’s the ideal analytics professional you want on your team.


[1] – “The Indian Equation for Advanced Analytics Talent” –, July 2019

Senior Partner | TheMathCompany

Ashish Sam