Product Management with a Data Science Mindset.

Science, do we know what it is when we see it? Photo by National Cancer Institute on Unsplash
  • All models are wrong, some are useful. (Attributed to George Box, British statistician.) Models fit to the data they are trained on, constrained by the learning algorithm and tuning parameters that guide the learning. Simply put, if you provide a learning algorithm with what it needs to run to completion, a model with be generated. That does not mean it will be useful. So ask questions and gain an understanding of the assumptions, where the solution fails, and how to monitor for degradation.
  • We often formulate our opinion and then look for data to support it. A good data science team should have rigorous validation and testing methods to support their assertion that the solution is what you need. Understand the assessment that the solution went through and ask yourself if this will support your use cases. Everyone should be aware that time pressure, and faith in iterative upgrades, can lead to the implementation of a bad solution. Question everything before it goes into the product. (An interesting side read on how we cling to our opinions: Facts don’t change our minds)
  • There are ways to measure bias. If you are in the business of putting models in products, you should already be aware of the problems of bias. Racist and sexist models have been in the headlines and they have real consequences. Make sure the DS team has tested for bias and that you have a plan to continue testing.

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Tyna Hope

Tyna Hope

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