From Doing to Enabling: The Data Science Leadership Transformation
This post is part of the "Becoming a Data Science Manager" series, where I explore the nuanced journey from individual contributor to data science manager, sharing insights and strategies for success learned from the trenches.
TL;DR: Moving from data scientist to data science manager requires a profound identity shift. This post provides two practical checklists to gauge your progress: one for overall managerial mindset (are you transitioning from doing to enabling?) and one for technical leadership (are you letting go of hands-on work?). Bonus: specific action items to fix any red flags you discover.1.
I’m not a big sports person, but if I had to pick one, it would be football. And in decades of casually following the sport, I’ve noticed something interesting: It is very rare for star players to successfully transition into being great coaches. One notable exception - unsurprisingly - is Zinedine Zidane.
Why is this transition so rare? What made Zizou different? And why am I talking about football on a data science leadership blog?
Because transitioning from a star individual contributor to an effective manager is just as challenging in data science as it is in football. And the reasons are surprisingly similar.
The Core Identity Shift: Doer to Enabler
On the field, Zidane was magic. He could single-handedly turn a match with a moment of brilliance - a impossible pass, a stunning goal, a game-changing play. His individual performance was core to his identity. Success was measured by his personal statistics, his individual moments of genius.
But coaching demanded something entirely different from him. Suddenly, success wasn't about what he could do, but about what he could make possible for others. His role shifted from being exceptional on the field to assembling a team of exceptional people, having them play well together and removing obstacles so that they could be at their peak performance.
As a fresh data science manager coming from being a star individual contributor, you have to undergo the same transformation. Where you once were the one developing elegant ML models, you now become the architect of a team that can consistently deliver breakthrough solutions. Your success is no longer measured by your personal technical output, but by your team’s collective capability.
Yes, there are a lot of new skills to be learned and that’s a challenge. But the real hard part is this: it’s a profound mental transformation. You move from being the person who directly solves problems to being the person who removes obstacles, provides clarity, and enables others to solve problems at scale.
Putting in Practice: Managerial Mindset Checklist
But how can you actually tell if your mindset shift is really happening? Here’s a quick way to check if you’re truly transitioning from star player to great coach. Many new managers default back to their IC comfort zone - this check will help you spot when that's happening. You should need no more than 15 minutes every Friday to honestly assess these areas. No overthinking - your first instinct is usually right.
The exercise is simple, go through these questions and count how many greens, yellows and reds you get.
Are You Still Playing or Starting to Coach?
Do you know the priorities of the business orgs you're working with?
🔴 You can’t immediately name your org’s top priorities.
🟡 You know them but would stumble explaining why they matter.
🟢 You can explain priorities and connect them to your team’s work.
Do you know what projects your reports are working on and how they are going?
🔴 You’d struggle to give a quick summary of anyone’s work and capacity.
🟡 You know the projects but details are fuzzy. Better than red, but definitely not good enough.
🟢 You can give a 30-second rundown of each person’s situation, complete with their achievements, struggles and blockers.
Do you know how your stakeholders are feeling about the projects your reports are engaged in?
🔴 You haven't initiated any stakeholder conversations in over two weeks.
🟡 You're in regular contact with some stakeholders but have gaps in others.
🟢 You have clear visibility into all stakeholder priorities and satisfaction levels.
Are you tracking your direct reports' professional development? Have you agreed on and are you monitoring their development plans?
🔴 Missing career goals or haven’t checked progress in a month.
🟡 Career goals exist (at least on high-level) with most reports and you have discussed progress with most of them in the last month.
🟢 Clear picture of where everyone’s headed, how they’re doing and how you can help them. You have at least monthly syncs with everyone to discuss this.
Do you have a clear idea about team health for your direct reports?
🔴 A team issue (conflict between team members, motivation issues, severe miscommunications, etc.) blindsided you more than once during last quarter.
🟡 When issues arise, you mostly are aware of them, but their severity sometimes still catches you off-guard.
🟢 No surprises - you see issues coming and handle them early.
Count your colors every Friday. If you have 4 or more greens, congrats, you’re going strong along your managerial journey. Having 1-2 yellow items is acceptable—perfect is the enemy of good. And reds need immediate attention.
Keep in mind that this is a journey, not a destination. You might be doing well this week in any given area, but let your attention slip next week and fall behind. This checklist is meant to regularly make you aware of your standing in each dimension.
As said above, reds require your immediate attention. Here’s your next step for each:
Still Coding: Block off time to delegate those coding tasks you’re holding onto.
Business Blind Spot: Get coffee with your business leads this week.
Team Confusion: Clear your calendar for focused 1:1s.
Stakeholder Sync: Schedule all key check-ins before EOD.
Growth Gap: Block time next week to update career plans and/or have check-ins.
Missed Conflict: Create extra team touchpoints until you’re back in sync.
Fifteen minutes every Friday. That’s all it takes to stay honest with yourself about how you’re transitioning to a managerial mindset.
From Shining to Letting Shine
While the shift from individual contributor to manager is challenging across all professions, data science presents a unique wrinkle that demands deeper exploration.
As a data scientist, you've likely spent years honing your technical expertise - mastering complex algorithms, developing elegant solutions, and being recognized for your technical brilliance. In fact, it's probably this technical excellence that earned you the promotion to management in the first place.
But here's the paradox: the very technical mastery that got you the management role is what you now need to (partially) let go of. This isn't just about time management - it's about fundamentally redefining your relationship with technical work. Instead of being the technical expert who implements solutions, you need to become the enabler who creates an environment where others can develop and demonstrate technical excellence.
Putting in Practice: Technical Involvement Checklist
Let's see how well you're navigating this transition with another 15-minute Friday checklist - this time focused specifically on technical leadership and enabling others. This is again a quick test that you can use to gauge if you’re becoming the enabler for your team.
How hands-on are you with project technical details?
🔴 You’re deep in implementation details in every project. You could sub for a report when they’re out sick.
🟡 You’re balancing between strategic and tactical, but still too hands-on. You can clearly explain details of most implementations.
🟢 You’re focused on architecture and strategy, team handles implementation. You don’t need to know all the details.
How does your team handle technical decisions?
🔴 Team constantly asks for your input on technical decisions and you often are the bottleneck in the process.
🟡 Team knows what they can decide but often seek unnecessary validation for their decisions from you as a form of insurance.
🟢 Clear framework exists, team confidently makes decisions at their level and knows when to escalate to you.
How is technical knowledge shared in your team?
🔴 You’re the go-to person for technical knowledge, since you were recently the star individual contributor and you know everything. No significant documentation exists.
🟡 Some documentation exists but you still get requests for knowledge sharing almost daily.
🟢 There is complete documentation for all the important processes and systematic knowledge sharing practices are in place. Sometimes weeks go by without anyone asking you for knowledge sharing.
How does your team handle technical emergencies like downed production system?
🔴 You’re called in for every case, nothing happens without your involvement.
🟡 Basic protocols exist for who should be involved in what, but team still relies heavily on you to guide them in emergencies.
🟢 There are clear guidelines for emergency handling and escalation, and the team mostly handles emergencies independently.
How do you maintain technical quality standards?
🔴 You’re reviewing all big technical decisions.
🟡 Standards exist but you have to micromanage whether they are observed.
🟢 Team maintains quality through clear standards and peer review without your involvement.
As above, you can feel good about your progress if you score 3+ greens weekly, 1-2 yellows are okay and you have to work on fixing reds immediately. Here’s your next step for each red area:
Technical Deep-Dive: Document the decisions you make this week and create delegation plan.
Unclear Authority: Draft and share decision-making framework with the team.
Knowledge Bottleneck: Set up systematic documentation and knowledge sharing practices.
Crisis Management: Define what constitutes an emergency and document escalation paths.
Quality Control: Create clear quality standards and review processes, then step back.
The Long Game
You might be looking at those checklists thinking "I'm in the red everywhere." If you're feeling overwhelmed - that's normal. Like Zidane, you're facing that jarring shift: one day you're on the field scoring impossible goals, the next you're trying to orchestrate an entire team's success.
The good news is that the hardest part is making that mental switch from performer to enabler. Once you internalize that shift, the rest becomes a matter of practice and persistence. Do you know how long it took Zidane to evolve from world-class player to Champions League-winning coach? Six years.
So don’t be hard on yourself. Run those Friday checks, keep score of your progress, and trust the process. You've got this.
Your Leadership Journey Matters
Which checklist item hit closest to home for you? Share your biggest struggle in the comments - I'll respond with targeted strategies I've seen work in similar situations.
Next week we'll deep-dive into some of the challenge areas. I’m thinking of starting with technical delegation. Which area would you like to see discussed next?
While I've written about this before, the complexity and importance of this transition deserves a deeper dive. This series will focus on providing concrete, actionable guidance for navigating this critical career shift.
Meaning coding time that is not solely dedicated to handing over your old projects.
Note: These metrics are calibrated for typical data science teams of 7-12 people. For smaller teams (3-7 people), you'll need to maintain somewhat higher technical involvement - aim for <40% coding time. For larger teams (12+), target even less hands-on work (<10% coding) and focus more on organizational alignment and strategy.
These targets come from my experience leading multiple DS teams and guiding multiple IC-to-manager transitions. The coding threshold especially has proven critical - in every case where a manager stayed way above given optimal coding time, they struggled to provide adequate team support and stakeholder engagement. Beyond just time constraints, heavy coding involvement prevents managers from developing the strategic mindset needed for their role.