Fellow data scientists, have you ever wondered what your biggest motivation is? Is it to hit that ever-elusive target right before the submission deadline? Or is it something more along the lines of implementing and testing that fancy but obscure algorithm you saw on KDD?

Data science in the wild

More often than not, you will find yourself driven by these seemingly opposite motivations, especially during the application process for your first data science (DS) job after graduation. Consulting companies usually focus on delivering something data science-y that meets well-defined criteria. On the other hand, other companies have data scientists that mostly concentrate on applying (or beating!) state-of-the-art algorithms used in their industries.

So, the question is, can we aim to do both? Can we deliver results and pave the way forward?

A Shiba Inu dog with dialogue regarding the perceived chasm of data science
The perceived great chasm of data science: delivering results or paving the road ahead. Photo courtesy of the Internet.

That’s where data scientists come in.

There are different types of data scientists. It’s always fun to reflect on how their responsibilities are viewed from different angles:

Delivery data scientist

  • What customers think I do: solve all the problems, like a hero, while the clock is ticking.
  • What other DS teams think I do: attack problems by brute-forcing more features and models.
  • What I actually do: reply to customer emails about weird data issues.

Research data scientist

  • What customers think I do: build algorithms that will take over the world and make all jobs obsolete.
  • What other DS teams think I do: scribble advanced math completely detached from actual problems.
  • What I actually do:
Example of what research data scientists do
And probably raise an exception right after.

Product data scientist

    • What customers think I do: provide them with a polished product.
    • What other DS teams think I do: put on a façade.
    • What I actually do: spend a substantial amount of time in front of a whiteboard with post-it notes, figuring out priorities.


It seems we tend to categorize data scientists based on our perception of their skillset. Just search for job postings that call for any of these data scientist roles, and you’ll see what I mean. (I must say it made me chuckle when I noticed that “perform A/B testing” was the skill most commonly asked of “product data scientists!” ?) However, what actually distinguishes different data scientists from one another is what they do — not how they do it.

Chart that shows differences between delivery, research, and product data scientists
The main difference between different kinds of data scientists has more to do with the contributions they make toward the goals of the company and less to do with how they do it. Think about what drives each type of data scientist and what their output is.

The key lies in the function

These three kinds of DS roles almost look the same from a technical standpoint. In fact, data scientists in these positions share nearly identical skillsets and carry out similar tasks. When we zoom out and see how these various data scientists contribute to companies, however, the contrasts rise to the surface: there may be technical overlap between different roles, but there are some significant functional differences.

So, what do all these observations tell you? If you feel that there are now more questions than answers, rest assured. Everything is going as planned. In the next installment of this 3-part blog series, we will discuss what the focus of DS should be on a product team — practical examples included!