Sprint 1 — Intro
I am an ML product manager. What does that mean? Well, I do all the regular PM stuff such as:
- talk to customers,
- identify requirements,
- write specs and stories,
- prioritize features,
- work with Dev and QA to implement the feature…
Also, I have a technical background that includes image and signal processing, machine learning, engineering. Prior to working as a PM, I worked as a data scientist (DS), before it was commonly called that. Which means I also:
- consider what problems should have ML in its solution,
- work with DS to make their work productizable,
- work with Dev on ML specific infrastructure,
- help QA test the ML solution and
- explain the solution to customers in a way that they understand.
So, I fused two really cool jobs and it turns out that this is really useful.
I was recently asked to talk with a group of aspiring Data Science professionals to help fill the gap in their knowledge about products.
Hmm… where to start.
I spent about 30 minutes chatting with them and I feel 100% certain (+- 5% CI) that
- I could have talked longer and
- I already gave them way more than they could digest.
Here are some of the highlights of the discussion:
- why products are special consumers of ML
- what a DS in product probably does and doesn’t do
- the difference between DEV, QA, DS, and PMs
- what I actually spend my days doing
- non-DS roles in product companies for data scientists
- AND, how I got here.
So if you are interested in learning more about my thoughts on this; please join me.
