Katie Bauer!

What is the difference between an engineering manager and a data science manager? It's a question I find myself ruminating over almost constantly. There's tons of good thinking and writing about eng management out there, but I don't find that it always translates to the DS world.

My suspicion is that most eng management advice can apply to Type B DS management pretty readily. Their work is more purely engineering. But what does that mean for Type A DS management? What makes it different and thus hard to apply the same advice to? I have some thoughts.

To start, DS is not essential like engineering is. Yes, a product will be worse without analytics on how it's performing, but it can still ship. Everyone loves to talk about being data driven, but many people still operate on intuition and feel.

It's easy to find ways to a feature launch a success, especially if basic metrics are neutral to positive. Who cares if the differences are statistically significant? We shipped fast! We paved the way for future innovation! And it is fun to use and looks great!

A DS can do tons of analysis to support a product area or launch, but that work could be largely ignored. As a DS manager, you are stuck wondering--was that because the DS failed to understand their customer's needs, or did their customer not use that work for some other reason?

This happens to engineers too, but it's not as common. Not everyone sees a DS's time as precious. It's not that they don't appreciate it it, but rather that it's a value-add. If DS work doesn't pan out, oh well! We didn't REALLY need it.

And for Type A DS managers, this makes measuring the contributions and progress of your team extremely difficult. The value your team adds is often realized by influencing someone else to act. It's latent, a second order effect. It takes time to see.