tl;dr

• North star: Happier teams deliver more.
• How I sail that course:
  • Remove demotivating emotional shielding, using empathy and vulnerability to create a safe space.
  • Motivate by providing purpose, autonomy, and the chance to develop mastery.
  • Don't control: trust.

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I've led teams.

• Data teams: AI engineers, machine learning engineers, data scientists, data engineers, labelers, business analysts, data analysts, statisticians.
• Classrooms: from high school students to graduate students.
• Naval petty officers and seamen: submarine hunters, air controllers, firefighters.

Start with having clear goals.¹ However, goals don't motivate.

What motivates? I focus on happiness for two reasons:

1. Happy workers are more motivated. They are also less likely to leave, achieve more, take less sick leave, and help each other more.
2. I like working with happy people.

What about more money? That seems easier, and it works, right? Sometimes, sometimes not.

• Example: Performance bonuses generate higher performance for simple, repetitive, mindless tasks like data entry.
• Counterexample: Children who enjoy drawing for fun may lose interest if they start receiving rewards for it, as the activity shifts from play to work.

Even when it works, money can have limits as a motivator. Past about $105k/year, higher pay doesn't increase a worker's happiness.² So what does?

I lead with empathy. Your team will be more likely to let themselves be vulnerable if you do.

When I had a major illness a few years ago, I told my team about it, that I was scared. I didn't cast the future as dismal, but I was honest (without oversharing) about my uncertain prognosis. I shared my concerns about how it would affect my work and career. I told them I was confident in my leads and the whole team, that they would step up while I recovered.

Several team members thanked me privately for being open. Some shared that they or someone close to them had been through a similar experience. My team did step up, knowing I believed in and counted on them.

More than once I've had a team member break down in tears during a weekly one-on-one because of a personal situation. I'm glad they felt comfortable enough to open up to me.

Empathy leads to a happier team and better performance

Feeling safe at work has well-known performance advantages.

• It's one thing to say, "We don't focus on blame, we focus on preventing the next problem." That only works if your team feels safe owning their mistakes.
• When reorganization hits, those not directly affected are often afraid, not only for their own jobs, but also for their development and promotion. I recently left a job I loved and where I was succeeding because leadership left me in limbo for a month after a reorg was announced. By the time they said I and my team were valued, I had already received a better offer elsewhere.

Having a purpose that aligns with your values is deeply satisfying. Here are some jobs I've had, and the stated purpose of each organization.

• US Navy: Defend freedom, preserve economic prosperity, and keep the seas open and free.
• National Public Radio: Create a more informed public.
• Revecore: Help hospitals thrive so they can continue to serve their communities.

Am I ready to step up and give high effort for those? No doubt.

Admittedly, an organization or culture can manipulate those in some professions (teaching, child care, nursing) using vocational awe by citing a higher purpose. I try to avoid setting that tone, and sometimes I'll point out that "There are almost no emergencies in data science."³

I hire professionals.⁴ Professionals have knowledge, skills, and experience built up over years. It is wasteful and insulting to tell them how to get something done.

A supervisor can do even better by asking teams to choose what to work on, or at least provide input; this is better management of their own time and better leadership by motivating the team.

A good practice is to set requirements and leave the solutions to the experts.

• Motivating: Maximize revenue by improving the recommendations while not reducing margin, not increasing the budget more than 10%, and maintaining security. You tell me how to do it.
• Demotivating: Improve recommendations by adding these features, using this algorithm, and using this architecture. I'll tell you what to work on first.

FunFact: Most workers, professional or not, prefer to have some autonomy in their jobs.

Professionals of all stripes have specialized skills. In data science and other data fields, those skills require constant learning and the chance to apply them.

Some specific ways to encourage developing mastery:

• Support training and conferences with time, money, and inclusion in evaluations of performance.
• Share lessons learned with each other.
  • Ex: AI is changing how we work. Each week, each member of my team is invited to update a Confluence page with a very brief lesson they've learned about trying to use AI in their workflow.
• Set an example. When I present a possible solution that I just learned, I mention that I just learned it. It may help them see that I keep working at improving my technical skills.
• Hold lunch-and-learns with folks across the organization. Team members learn more by preparing to present, and they improve their presentation skills.
• Ask for help. If you create a safe space and then admit you don't have all the answers but want to learn.
• Assign tasks that (a) leverage existing skills AND (b) require a new skill.

Consider this: Your team likely won't retire from your current organization. They're going to be on the job market at some point. Are they burnishing the skills that will help them find that next job? If so, most will feel they are growing, safer, and will be less likely to leave. I have left jobs before because they focused too much on developing skills that wouldn't transfer to other roles.

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1. More on data team goal definition in future posts.
2. In 2010, "enough" was ~$75k/year in the US (Daniel Kahneman and Angus Deaton, 2010). In 2025, that's ~$105k/year.
3. I've dealt with real emergencies, like an engine room oil fire or recovering the bodies of friends who died at sea. Civilian data science doesn't usually justify much cortisol.
4. More on "professionals" in future posts.