Photo by Matteo Vistocco
The role of the data science manager is to provide group-based leadership with a data science focus. The data science manager must have a deep understanding of the data science process and the ability to lead and motivate data science teams. The data science manager must also work with business stakeholders to understand their needs and translate them into data science solutions.
The data science manager is responsible for the following:
- Leading and motivating data science teams
- Ensuring that data science solutions are aligned with business needs
- Coordinating data science work with other parts of the organization
The data science manager must have the following skills:
- Leadership and management skills
- Technical skills in data science
- Business skills to understand and communicate with business stakeholders.
The data science manager role is a new and growing role in the data science field. As data science becomes more important in business, the data science manager role will become more critical. But what are the components that make a data scientist manager? In this post, I am trying to discuss a few that I found the most relevant based on my experience in the past few years.
Be honest with people and willing to get hands dirty
This approach also promotes open communication between all parties. We’re more likely to uncover practical answers when the team feels comfortable introducing new ideas and discussing difficulties openly. No one has a monopoly on good ideas, and the idea hierarchy should be flat.
Openly disclosing company KPIs and performance data is also critical. People’s perspectives shift when they see their direct impact on the firm’s bottom line. They become more inventive, interested, and eager to try new things.
Set parameters
Being a data science leader necessitates regular team meetings, idea-generating sessions, and strategic project management. While data science directors may not be as hands-on as their team members, they cannot afford to ignore the day-to-day tasks. The position requires the leader to take on a new function, which includes gathering requirements, establishing general parameters, and providing the appropriate amount of feedback.
Give feedback often and help your team understand where they are and how they can grow, but don’t forget to ask for feedback. Yes! You need feedback to align yourself with the team and adjust your path.
Mutual trust
To ensure that continuous experimentation is successful, we must have faith that the team is working diligently every day to tackle the problems that have been entrusted to them. We must also have confidence that we have assembled the most incredible team possible. On the other hand, the team should realize that we are working as hard for them as they are for us. The group becomes motivated to explore and succeed due to developing mutual trust.
A big step toward trust is the feeling of being involved in decision-making. It is a normal human desire to be heard, so before making any decision, ask each of your team members their opinions and give them a chance to speak out regardless of their level on the ladder. It is usually impossible to oblige all the voices, but you can contextualize decisions, and your team will know that their opinion will be considered.
However, trust should not be blind. We can see the code while people are developing using software systems. At Companio, my team manages production-level techniques such as intelligent lead routing and lead bidding algorithms. I can see if my team is innovating and developing new approaches directly.
Stay curious
A good leader should look for new methods and think about how they may be used in the team’s work.
Growth will be limited if there is no curiosity. We are essentially establishing an atmosphere favouring high-impact experiments when we foster a culture of creativity in our data science departments. Many data scientists are naturally curious, so giving them the freedom to explore the depths of their thoughts is critical. Our curiosity as leaders influences how the team approaches daily difficulties and fuels their collective creative energy in constructing forward-thinking solutions.
Keep yourself informed about the higher business settings
In many companies, a data science team’s work has a ripple effect throughout the organization; our work interacts with other company-wide systems in potentially complex ways. We might improve a system that we think is isolated without knowing about systems integration. Still, such actions could have a cascading effect on other systems, processes, and people.
It’s critical to connect the overall impact and context to the bigger picture. So make sure every team member knows how their work impacts and helps others and the company as this is the best motivator.
Summary
Up to this point of this article, you have noticed the golden key is communication, or as I’d like to call it sometimes, “over-communication”.
Managers should be the most prominent advocate for the team members to thrive and grow, and transparent communication is the route to this goal.
A simple role in mind is that if you grew your team member to the point that they became a clear replacement for your position, you did your job very well as a forward-thinking leader. It might sound rough at first but try to sleep on it a bit, and you will see a true leader definition appears.