Building Data Science Teams: What Do You Need to Know?

Building A Data Science Culture Before You Hire

  • Prospects (pure acquisition): who is the next customer? Where are they coming from? What attracts them?
  • Customers: Who do we know? What are their needs?
  • Product: Where do they land? Where do they go? Why do they stop?
  • Content: what do they want? What do they need? What can we offer?

Building An Operational Flow As You Hire

https://bit.ly/2Me4K6B

Retention and Goals

  • Impact: is building a model going to create a few percentage points of improvement or an order of magnitude? This helps you see how information is really being used, and it forces you to compare notes across the organization. This concept also helps you build risk management.
  • Interest: This area is a vital part of team feedback because boring models tend to be bug-filled. You can mitigate this type of work to maintain your momentum and morale.
  • Difficulty: Inconsistency across your team often appears in what one person finds difficult and what the other finds easy. Transparency here builds trust, which is a vital piece of recruiting and retention. Diligence and empathy for how you’re picking your models and deploying them within the team give members a sense of choice.

Model Review — Improving Business Objectives

  • How old are the inputs? How old should they be?
  • How old are the outputs? How old should they be?
  • Are the outputs non-missing?
  • Are the outputs present and diverse?
  • How old is the model? How old should it be?
  • Did the model actually update?

Building Overall Company Data Culture

The Takeaway

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