Data Science with a Product Management Mind Set

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.

Original artwork by my friend T. Diamantis.

An ML product manager. Bio on LinkedIn. Opinions expressed are my own.

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