Why good data scientists make good product managers (and why they’ll be a little uncomfortable)
When I was transitioning my career from data scientist to product manager, I solicited a lot of feedback from current data scientists and product managers about getting in touch with others who had attempted such a transition.
I was surprised by how often I heard some variant of “Hmm, I don’t know anyone who’s made this transition, and it seems a little odd to me.” I’ve always thought that the best data scientists are product-focused and have users and their needs in mind. Training models for the sake of training models isn’t really useful until they can be productized. To me, it seemed a perfectly natural transition.
I wasn’t discouraged, and I’d like to offer some perspective on what the transition is like for the benefit of others who may be thinking of either making this transition themselves or for hiring managers who are considering hiring a data scientist as a product manager.
A major part of a data scientist’s job is choosing between competing options by identifying the relevant evaluation metrics, predicting the potential impact of a particular intervention, and communicating those results to stakeholders in a clear and concise fashion, pitched at the appropriate technical level.
Product management is no different. You’ll need to know “how will I know if my product is successful”, “how much of an impact do I think this new feature will have”, and you’ll need to communicate the why to both senior executives and junior engineers. Plus, data scientists already know SQL and can do their own quick analyses. They know not to overreact to variance in their metrics and know that experiments are a good remedy to reading tea leaves in a random walk.
Most data scientists are used to working across teams with colleagues in differing roles, from marketers to engineers to designers. They know they’ll need to think about things like how to serialize their models and how to surface the predictions of their models to users. They know they can’t just build a model and throw it over the wall to engineering to reimplement.
Product managers jump from writing and explaining acceptance criteria and specs to engineers to reporting on the performance of their products to working through wireframes and mockups with designers. They know they need to be able to be technical enough, business-oriented enough, and design-focused enough if they want to ship their product.
Data scientists and product managers choose an objective function and ruthlessly optimize for it.
Good data scientists know that optimization problems always involve tradeoffs. Want to maximize clickthrough rate for a particular part of your site? Prepare to see the clickthrough rate for another part drop off accordingly. Picking the right objective function is as important as finding the right features for your machine learning model and being explicit about the tradeoffs you’re making is the mark of a seasoned professional.
