5 Tips for Managing Data Science Projects
Data science has traditionally been an analysis-only endeavor: using historical statistics, user interaction trends, or AI machine learning to predict the impact of deterministically coded software changes. For instance, "how do we think this change to the onboarding workflow will shift user behavior?" This is data science (DS) as an offline toolkit to make smarter decisions.
Increasing, though, companies are building statistical or AI/Machine Learning features directly into their products. This can make our applications less deterministic—we may not know exactly how applications behave over time, or in specific situations—and harder to explain. It also requires product managers to engage much more directly with data scientists about models, predictability, how products work in production, how/why users interact with our products, and how our end users measure success. (Hint: most users don’t understand or care about F1-scores; they just want to get the right answer.)
So, here are a few tips for product managers who may be pulling data science—and data scientists—into our product processes.
1. Provide Much Deeper Context than Traditional Software Projects, Especially Use Cases and Business Goals
We’ve known forever that we (product managers) must include our developers and designers in the
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