Data Science and Product Management

There’s no point in using AI/ML just for the sake of doing something cool, and a good PM knows when to step back and admit that it isn’t necessary. However, if a product meets the right criteria, the best thing you can do is have a good working relationship between the Data Science team and PM.

Here are some tips:

- Treat your project as a partnership. Make sure everyone knows why you’re making your decisions the way you are. You should have a clear problem, hypothesis, and success metric. Start from there and let everything else come later.

- Be willing to make tradeoffs. Each one of you is there for a reason and their expertise. Understand the technological limitations and above all, understand your user. Just because the technology is new and exciting, it doesn’t mean that the product needs ALL of it to be effective.

- Remember to always be considerate: if you’re the PM, you need to be aware of a scientist’s time and momentum. It’s important not to expect them to work at your pace or presume to tell them how they should organize their time. And if you’re the DS, you must clearly communicate your work process and its limitations.