Sprint 2 — Do what you do
This post is the second in a series about being an ML PM, that started here.
As a product manager you are all about relationships. You don’t actually manage anyone, unless you are the lead/head/director PM. But to do your daily tasks, you are an influencer, a listener, and interpreter.
As an experienced PM, you have already mastered this in
- the business space (selling your features up),
- the customer space (eliciting feedback) and
- in the Dev space (getting your stories in the sprint).
Now there is one more space: Data Science.
The last article really focused on the skills that I think you need to break into the ML PM space. There are a few reasons for having these skills, one of them is to be able to communicate. You can’t have a relationship with someone that you cannot communicate with. There will be a lot of back and forth.
Example: The DS team have created a model or algorithm that works well but has very complex features that will be challenging to develop and even more challenging to QA. You are really concerned.
Your relationship with them is the only mechanism you have to convince them to simplify the design. Consider, they have put lots of time and effort into their solution and now you are asking them to change it because it does not productize well. This can be a difficult conversation.
This is where you leverage the skills that you have already honed. Use the same skills you use to handle all the other problems you solve.
- Understand the problem.
- Communicate it in a way that it is understandable.
- Listen.
- Be empathetic.
- Work together.
You will be very focused on the productization problem. The DS team will be focused on the problem they are tasked to solve. From the above example, as the ML PM you need to be sure that you understand the solution the DS team is presenting to you, Be careful that you are not assuming complexity, there may not be an issue scaling to production. The complex bit may be two lines in a common python library (so no problem) or 200 lines of a home grown algorithm (likely problem).
Once you have decided that there truly is a productization challenge, Discuss it. Be clear and specific when communicating the scaling issues. Just the facts. If one feature is the issue, focus on it and work together to come up with an alternative. It may be that the one tricky item can be swapped. Remember you are the product expert, the data scientists may not have considered the challenges that you are seeing.
Listening sounds like it should be easy, but its not. As PMs, we learn the skill of not only hearing the words but understanding the point of view. Getting to the root of the real problem, not just taking the initial solution people say they want. You would be surprised at how much you can help with the data science part. The listening and being empathetic go hand in hand.
While getting to the end solution can be tricky, working through it together is the only way forward. After all, as an ML PM, the data science space is but one more space where you do what you do.
