Data science is booming worldwide, IBM predicts that there will be ~2.7 Million opportunities in the industry by 2020. Many companies believe that this discipline will be the future of decision making for their leadership teams. Because of this, there has been a meteoric increase of data science jobs and an influx of data scientists.

It is usually an afterthought, but the question arises: who can manage and lead this group?

Great Data Scientists Don’t Always Make Great People Managers

The ability to do quality technical work is not highly correlated with management. Data Science is no exception. What makes a great data scientist does not necessarily make a great data science manager.

While some data scientists work in teams, a large portion of data science work is spent in isolation. Great data science performers can dive deep into a specific problem and surface with elegant insight. Managers, on the other hand, must understand many projects and concepts at a somewhat shallow level. For many data scientists, this high level understanding of problems will not be stimulating or fulfilling.

Additionally, the way that managers interact with people within organizations deviates from the traditional data science role. Managers have to be comfortable working with different teams, dealing with office politics, and selling the success of the work being done. Often, data scientists have limited exposure to stakeholders and do not want to be involved in the corporate merry-go-round.

This being said, data scientists can still excel as managers if they have developed the skills that come with working in cross-functional teams. Even with the proper aptitudes, this group may not be interested in the types of problems that managers deal with on a day to day basis.

Managers From Other Business Units May Be Out of Their Element

People with general management experience are also prone to struggle in this role. Managers coming from software engineering or business side functions (finance, accounting, etc.) can have trouble understanding data scientists and defining their project work.

The background of data scientists is overwhelmingly academic compared to that of software engineers or workers from other business functions. Managers coming from other disciplines can struggle to relate to this group. Most other business functions focus first on implementation, however, data science is grounded in theory. A manager needs to be able to work with data scientists to take problems from theory to practice.

Often times, theory is a fundamental component of data science projects. I look at these projects as a hybrid of software engineering and consulting tasks. To generalize, software engineering is about setting a great plan and executing on it; consulting is about gathering information and crafting a recommendation. Data science work captures both of these elements. Data science managers need to understand that these projects are fundamentally different than the ones that they have worked on before.

Data Scientists Create Wonky Power Dynamics

Data scientists are usually very intelligent and can come from a range of backgrounds. It is a field that comparatively has an over-representation of academics. The academic culture that some of these employees bring can occasionally clash with a traditional business workplace.

Data scientists can also vary significantly in age; many managers are younger than their data science employees and may even make less money. While it may not be kosher to talk about, this can create some friction in the workplace. I don’t believe that age or pay should impact on work relationships, but without a doubt, they do.

Who should manage this group?

To be a successful Data Science Manager it takes three things: (1) An understanding of the domain and algorithms (2) An understanding of the nature of the projects (3) The ability to manage people and stakeholders. Someone with this trifecta is extremely difficult to find; however, there are a few logical places to look.

Experienced data science managers — You may be able to poach one of these from another company. If there is something enticing about your offer, you can lure one of these unicorns away from an existing role.

Home-grown — It takes a conscious effort to develop the soft skills of the data scientists that you currently have. If you do this well and some of the team is interested, you may be able to breed a winner.

Data Science Consultants — Data science consulting has grown in popularity proportionally to the rise of the field. People in these roles generally work on shorter term projects and have to interact more frequently with stakeholders. They are forced to cultivate many of the soft skills that managers require. If they have been working in this role long enough, a transition to management is a logical next step.

This is not to say that only people with these backgrounds can be successful managers. These are just the most consistent places to look in my opinion. Happy hunting!