TLDR; you can be a data science manager and still get things done by yourself.
It is a big decision for you to change your role and become a manager. driven by your natural curiosity, you don’t want to stop learning new skills and of course, you don’t want to lose the skills you developed as an individual contributor. I assume also you have a great sense of ownership, however, you don’t want to micro-manage your team writing your code line by line. In this article, I suggest a few ideas to help you keep updated and motivated to do data science as a manager.
How can my technical skills help my team?
As a manager, you should have faced tons of errors and bugs, from your experience you are able to find creative solutions to save the team’s time and expand their horizon. The Data science world is changing every day, if you can be your company’s eyes to the outer data science world, everyone will appreciate that, especially your team members.
Also, there are a lot of tasks no one has time for however it is very important for teamwork. In general, there’re many unfilled gaps in the data science teams as the Analytics area has seen more focus recently, these gaps need new jobs to be created within the team to be filled, for example, data scientists had to do a lot of data engineering work a few years ago until the title was defined and adopted by many companies. You may need to step in to fill one of those gaps and become a business analyst, data architect, or product owner in one project.
What can you do to help?
- Explore new technologies: Recommending new ways for doing tasks is one of the great managers’ qualities. It shows both creativity and deep technical knowledge when done right, this is one of the easiest things to do to gain the respect of both your team and your stakeholders. It is also a fun thing to do and helps you to keep yourself updated and learning.
- Solve team challenges. Understanding your team’s challenges and working on them yourself helps you to keep on top of your team’s work as it helps you to understand the details without writing the whole code yourself. You will not have the time to work on every problem yourself, but it is great to focus on the main challenges and start working on long term solutions.
- Explore what other teams do. Explore what other Data Science teams do inside and outside your organization. Don’t fall in love with your ideas, also, don’t fall in love with your teams’ ideas. Read technical blogs, ask questions, or reach out to your old teammates. This can be a win-win situation; see what problems they are facing, maybe you already have solutions, and ask about possible solutions to your challenges.
- Organize work: while everyone’s head is down coding, there are many tools that can help organize work no one will have time to explore, maintain and ensure that everyone in the team is using them. Version controlling is one of many examples, how the team projects should look like, what is the process of updating the repo… etc.
- Ensure scalability & reliability: No one likes slow and unstable solutions, as a manager you like to increase the adoption of solutions built by your teams, this can’t be done without building high-quality solutions, your role is to predict what issues solutions built by your team may face proactively.
- Automate manual tasks: What you can learn from data is limitless but our team’s bandwidth is limited. You should be able to identify what should be automated either: Internally: By creating templates for slides or dashboards, or by creating code and packages to be used by your team. Externally: By automating tasks when it is possible to automate, like self-service tools for dashboarding, querying data, and generating high-level insights