Building a Data Science Team

Just like other fields, data science has evolved over the past few years. One of the most evident aspects of this evolution is that data scientists are found in teams nowadays. Even consultancies are often team-based, enabling them to undertake a whole project flexibly and efficiently. But how do we build a data science team exactly? First, we need to look at the different types of data scientists and explore the different specialization levels such a professional may have.

Nowadays, there are several types of data scientists. The most important of them are the data engineering (delving into low-level tasks, such as ETL and handling any cloud-related operations) and the data modeling expert (usually referred to as just data scientist or machine learning expert when it's more specialized). Additionally, there are the data visualization expert, the data science manager, and the data communicator (a more niche role that's not as widely spread). Of course, depending on the data science area that a data scientist specializes in, there is also the NLP expert, the A.I. expert, etc. So, it's safe to say that the data scientist role is quite diverse these days.

Speaking of specialization, that's a topic on its own that plays a role in data science work. The specialist is the most common scenario, whereby a data scientist is really good at one particular task and fairly mediocre in other tasks not related to that task. On the other hand, a generalist is quite decent in various tasks but not particularly good at any specific task. Such a person may be a good team leader, but wouldn't be ideal for tackling a particularly challenging problem. Beyond these two, there is also the versatilist, who is quite good at one (or more) tasks but also quite decent in other tasks. It's like a combination of a specialist and a generalist, making an excellent asset in a team, especially in data science work.

So, how do we go about building a data science team? The team's specifics always depend on the project at hand, but in general terms, you can build a team as follows. For starters, you need to get a versatilist or experienced generalist as the team leader. This person can help build the team by finding professionals with a similar working style and cultural fit. Having a second generalist or versatilist may also be useful, depending on the size of the team. Additionally, you can have two or three specialists, one of whom would need to be a data engineer. If your team needs to work with clients directly, you may need to consider having a data communicator. Also, if the team's expected outcomes are more geared towards dashboards and graphics, you may need to have a data visualization expert onboard.

Should you wish to learn more about this topic and other organizational aspects of the data science field, you can check out the Data Scientist Bedside Manner book I co-authored last year. This book examines various aspects of data science work, focusing on the non-technical ones and various useful tips as to how you can improve your data science career. Check it out when you have the chance. Cheers!