Getting Data Science Right: How To Structure Data Science Teams For Maximum Results Posted on : Jun 04 - 2018

Data scientists have become the darlings of today’s competitive job market. Entry-level salaries can range into six figures, and roughly 700,000 job openings are projected by 2020. There’s good reason for this spike in demand, too. The job of data scientists is to extract insights which are hidden inside mountains of data, which can then be used to achieve diverse business goals ranging from fraud detection to face recognition. Far from being uniform, the field of data science is now as diverse and varied as the business goals which it helps achieve. Acknowledging this is key to building data science teams, which must be comprised of individuals with highly specialized (and complementary) skill sets in order to be successful.

The specialized, complex nature of data science work poses a significant problem for hiring. In fact, there is still genuine confusion in the job market about what the term “data scientist” actually means. At Ancestry.com, where we’re working with an enormous 10 petabytes database made up of millions of DNA samples and family trees, we resolve this confusion by specifying three major roles within the broader data science organization.

Organizational Structure For Data Science

There are often very specific technical requirements that different roles within the data science organization demand, but there needs to be a common understanding of what is required for a data science team to be successful. While the type of technical skill set is critical for a successful data science team, more importantly, the success is dependent on how the team is structured.

At Ancestry.com, our main goal is to provide meaningful, valuable insights to consumers about who they are, how they connect to today’s society and how they have been shaped by human history. We have three categories of individual contributors within the broader data science organization that make these findings possible: data scientists, data engineers and machine learning (ML) engineers. We define these roles as follows:

Data Scientist

The emergence of big data has allowed businesses to answer a wide-ranging set of fundamental questions that were previously unanswerable. Roughly speaking, the size and richness of big data have enabled the identification of structure within the data itself. For instance, one might ask, “What are the odds that a given customer opens a promotional email?” One can imagine that this probability can be estimated by way of its relationship to the customer’s particular characteristics and that this relationship can be derived from copious data — one approach might be to quantify the average behavior of all customers who share similar characteristics. Identifying this relationship and the relevant characteristics is the job of the data scientist. View More