There were so many great follow-up questions that we’d love to keep this conversation going and dive deeper into specific topics through the separate threads below:
- What is a symptom that you have observed, during your time in this field, of a team being low on credibility within an organization or with stakeholders?
- How can we build credibility and maintain credibility once we have it?
- What are data teams like, that are able to be pretty effective, impactful, and able to really make a difference?
- What is your perspective on team characteristics that allow resilience in the face of delivering negative results, or being able to handle that well?
- Data scientists sometimes have a reputation for valuing their autonomy or wanting to spend their time on fancy algorithms over delivering valuable results. Do you think that’s fair? How do you deal with this?
- What contributes to data science teams that collaborate well with non-data stakeholders?
Audience Questions:
- What tips would you provide for organizations where data science is not fully established?
- How do you evaluate data science candidates for roles?
- What’s the best structure for a data science team? Should we have a centralized data science team or data science teams that are located within products?
- I’m currently a data scientist and I am interested in moving into leadership. What do you think I should do?