Think about data science as split into Analytics, Algorithms, Inference

  • data-science
  • idea
  • winter

The main takeaway I will share is that companies consider three tracks of data science work to meet the needs of your business – Analytics, Inference, and Algorithms.

  • The Analytics track is ideal for those who are skilled at asking a great question, exploring cuts of the data in a revealing way, automating analysis through dashboards and visualizations, and driving changes in the business as a result of recommendations.
  • The Algorithms track would be the home for those with expertise in machine learning, passionate about creating business value by infusing data in our product and processes.
  • The Inference track would be perfect for our statisticians, economists, and social scientists using statistics to improve our decision making and measure the impact of our work.


  • Analytics – defines and monitors metrics, creates data narratives, and builds tools to drive decisions
  • Foundation – demonstrates ownership and accountability for data quality and code (expected for all tracks)

Business (expected for all tracks)

  • Ownership - Able to drive projects to success, enables others, owns impact
  • Influence - Communicates clearly, demonstrates teamwork, and builds relationships
  • Enrichment - Contributes to team-building through mentorship, culture, recruiting, and diversity efforts


  • We encourage team members to be generalists as well (sometimes that's a point of confusion).
  • We didn't start to specialize until around 2015 when our team was 30 people.
  • Analytics experts understood they would have less impact if they tried to apply machine learning to the business problems they were working on.