Going from an individual contributor (IC) to a manager is a logical career move for many. But the skills don’t necessarily overlap.
Take it from Emma Grocutt. She’s a data science manager at Trupanion, and when she first transitioned to a managerial role, she realized her daily tasks were much different.
“While the knowledge you gained as an IC will create a great foundation for making technical decisions as a manager, the skills required for leadership are significantly different,” Grocutt said. “It’s essential to prioritize learning new leadership skills.”
What do those skills look like?
According to Siddarth Ranganathan, Nintex’s director of data science, BI and analytics, the first big adjustment is accepting that you no longer have direct control over your tasks and actions — you instead serve as a proxy representative for your team.
“Your biggest challenge and opportunity is to figure out how to effectively delegate, inspire and empower your team to deliver high-quality work in a timely fashion,” Ranganathan said. “The key to making this happen is to actively listen, encourage, inspire, assist, remove obstacles, be compassionate and empower your team to excel and always grow.”
Are you about to make the jump from data scientist to manager? Read more about what these managers told Built In Seattle to ensure the smoothest transition.
What appealed to you about managing a data science team?
When I joined the data science team at Trupanion, I was only the third team member. I’ve had the opportunity to help grow the team over the last few years and broaden the scope of the work we do. While I’ve really enjoyed tackling the technical challenges along the way, I have found myself particularly drawn to the challenges of building a strong team and ensuring our work has the greatest positive impact on the business. Becoming a manager seemed like a natural fit. I used to be a science tutor and through that I discovered I like helping people grow. Managing others seemed like a great avenue to do that.
I was also excited for the new challenges of a leadership role. I knew it would be a great opportunity to develop new skills and have more influence on the data science work Trupanion does. I was particularly interested in the challenges of approaching our team’s data science work in a holistic way, focusing both on the big picture and on the moving parts that it consists of.
What skills do data scientists need to develop when they move into a management role?
Decision-making. When you’re a team lead, you frequently have to quickly evaluate new information and decide which course of action is most likely to have the best return on investment (ROI), often in ambiguous situations or with incomplete data. So learning to be decisive, confident and assertive is key. But you also need to be flexible and have the ability to pivot when something isn’t working or new information comes to light.
In addition, general leadership skills are vital and have to be actively learned. Being able to communicate effectively, deliver feedback in a constructive way, advocate for your team and their needs, and create a safe environment where people know it’s OK to fail are critical for building a team people want to be a part of. You also need to be able to synthesize a lot of information from a lot of sources to keep your team moving in the right direction. It helps to stay organized. Keep notes, spend time planning before meetings and think about what the important questions are to ask. Ask lots of questions!
Your role is to create the conditions for your team to do their best work.”
What other advice would you give to a data scientist who is managing a team for the first time?
Your days look very different from when you were an individual contributor – you’re writing much less code, if any, and are spending more time in meetings and doing administrative tasks. While the knowledge you gained as an IC will create a great foundation for making technical decisions as a manager, the skills required for leadership are significantly different, and it’s essential to prioritize learning new leadership skills. Don’t skimp on this. Whatever time you invest will pay back dividends.
Your role is to create the conditions for your team to do their best work. This means trusting them to do their jobs, and acknowledging that they know more than you in some areas. It also means championing your team and their achievement to others in the company, removing obstacles in their way, and advocating for their needs. Finally, be proactive. Figure out which other teams in the company your work affects, and whose work might affect yours, and reach out to those team leaders to ensure you understand each other’s goals and processes. This is important for pre-emptively removing any roadblocks to your team’s progress. It’s also a great way to collaborate on new project ideas.
Trupanion is an insurance provider that helps cats and dogs across the world receive the best medical care.
What appealed to you about managing a data science team?
The move from an individual contributor to a managerial role can sometimes be a big change. It’s usually accompanied by an increase in scope which requires the ability to orchestrate many different things to work in unison. If you like conducting and bringing different parts, processes, tools, technology and people together, then a managerial role is certainly one way to go. It also exposes you to a wide variety of problems, people and personality types and forces you to get out of your comfort zone and engage your own problem-solving abilities (both technical and non-technical) through collaboration with cross-functional teams. It helps you sharpen your business skills, develop maturity, become practical and realistic, build empathy and compassion, mentor and help your direct reports, peers and colleagues succeed. All these facets appealed to me when I was given my first opportunity to lead a data science team.
What skills do data scientists need to develop when they move into a management role?
The equation changes when you move into a managerial role. You no longer have direct control over your tasks and actions – you only have indirect control of your work tasks by virtue of being a proxy representative for your team. Your biggest challenge and opportunity is figuring out how to effectively delegate, inspire and empower your team to deliver high-quality work in a timely fashion. Actively listen, encourage, inspire, assist, remove obstacles, be compassionate and empower your team to excel and always grow.
Sometimes a situation may call for more active listening, while other times it might be to provide a technical solution or a path forward. You’ll constantly use these skills in differing amounts to solve a wide variety of problems – some that might be technical in nature and others that might involve the personalities of people on your team. Remember, while you are managing a data science team, you yourself are learning from your team, your colleagues and your manager. You might get exposed to problems that help you develop your business acumen or situations that require a little more tact to solve.
Once you become a leader, you work for your people and not the other way around.”
What other advice would you give to a data scientist who is managing a team for the first time?
It’s important to know your strengths and weaknesses and generally be self-aware about who you are as a person and what makes you tick. Be aware of your biases – they can lead to assumptions and impair your judgment about a particular problem or situation. Don’t forget to ask for help when you need it. Chances are that others have experienced something similar and may be able to provide some assistance. Lastly, try to get feedback from your direct reports, peers and colleagues to help you become a better leader. A leader is different from a manager. Once you become a leader, you work for your people and not the other way around.
Nintex’s end-to-end process intelligence and workflow automation platform aims to transform the way people work by streamlining simple to sophisticated business processes.
