How Do You Identify the Right Data Scientist for Your Team?

Have you been trying to answer the question of what type of a data scientist would be the best fit for your team? Is there a single all-encompassing answer or does it vary based on the client objectives? Read on for some insight.

As businesses today understand the value their data can unlock, they are faced with an increasing need for people who can help achieve this – Data Scientists.

Data Science is an overarching term that includes such wide variety of skills that it is extremely rare to find everything in one person. It’s not surprising that Data Scientists come in so many flavors, often leaving hiring managers and business sponsors a little confused whether they are bringing in the right person into their team.

At Tiger Analytics, we have worked with more than 50 clients at different stages of their analytics journey and executed hundreds of analytics projects, both small and large. From all these interactions with clients, I’ve come to realize that there are multiple views on what makes a good data scientist. These views are shaped by what has worked in specific settings and there is no single correct answer that works everywhere.

In fact, we face this question time and again - Who will be the right fit for the nature of work with a particular client engagement? As we address this question multiple times, we observe a clear pattern regardless of whether we look at it from the perspective of the business problem or the skillset needed to address the problem. We see these skills and problems falling into two broad groups based on the objectives they aim to achieve for clients.

In this article, I share our learnings on this topic – what type of a data scientist would be the best fit for your team.

This involves deriving insights from the data so that they can be reviewed and acted upon by business users. So the models or solution frameworks need to be sufficiently transparent to them. Business Users should be able to understand and interpret the models at a high level. “Black box” models will rarely find acceptance.