We’ve written a number of articles on the APEX of Innovation about the growing demand for data scientists, and the various enterprise changes that result from more data-driven roles cropping up throughout the organization. And while most people agree that data science will be a thriving career for the foreseeable future, the nuances of working with these professionals are often less recognized.

As a recent Forbes opinion piece put it, “As every company becomes enabled by technology, and traditional businesses acquire technology companies, learning how to work with data scientists and other technical professionals is a necessary skill for a successful career.”

The article went on to suggest that the term “data scientist” encompasses a broad spectrum of roles and is frequently used interchangeably with non-technical roles for data analyst or machine learning engineer. In order to work well with these data-focused roles, the article argued, it’s important to clarify this confusion and explain the differences between these equally important functions.

Susie Sun, a data scientist at WhatsApp, explained how these positions might interact at an eCommerce business:

  • Data analysts’ output is data, and they answer questions like, “Given this customer funnel information, where are my customers dropping off?”
  • Machine learning engineers’ output is a model. They answer questions such as, “Given that I want to increase my customer basket size, how can I build or improve my recommendation engine?”
  • The output of data scientists’ work is insight. They answer questions like, “Given all my data, how can I improve profitability?” Drawing on past sales transactions, customer information and demographic data, data scientists can identify the company’s most valuable customers and make recommendations to target these groups more specifically.

In light of the distinctions above, in order to get the most out of data scientists, it’s important that companies first determine the critical questions the business is facing—and whether they have enough data to provide insights into these questions.

It’s also critical that data scientists be given the freedom to explore data on their own. As the Forbes piece states, “To have the most productive relationship with a data scientist, present the problem and ask them to find a solution, rather than presenting your own…Data may be the new oil, but if you do not know how to use it and work with people who do, it is just a collection of meaningless facts.”

For more on the data scientist career path and some chief responsibilities check out this recent APEX of Innovation post.