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.
Technical
- 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
Also:
- 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.