Webinar Recording: Building Effective Data Science Teams

[EconomiCurtis]

Recording of Webinar:

Building Effective Data Science Teams

Featuring:

• Kobi Abayomi / Warner Music Group
• Gregory Berg / Caliber Home Loans
• Elaine McVey / The Looma Project
• Jacqueline Nolis / Saturn Cloud
• Nasir Uddin / T-Mobile
• Hosted by Julia Silge / RStudio, PBC

What you'll learn:

At RStudio, we believe successful data science teams require the right technology: open source, code-first data science frameworks based on R and Python, centralized on-prem or in the cloud for ease of collaboration, sharing and administration. However, technology on its own is not enough. It is just as critical to focus on the people in your team. To have a sustainable impact on decision making in your organization, you need to build a productive, effective, and collaborative data science team.

In this panel webinar, you will hear from leaders at Warner Music Group, T-Mobile, Saturn Cloud, The Looma Project, and Caliber Home Loans, about what it takes to build a successful data science team:

• Their experiences and perspective on building great data science teams of both R and Python users
• How their teams prioritize and deliver work in a way that adds value to their organizations, while also maintaining credibility among stakeholders even when the results aren’t necessarily good news
• Their advice for advancing your own data science career and collaborating with non-data-science stakeholders

[supermdat]

Really great session today! I found it super useful and well done. Big thanks to all the presenters and organizers!

[rachael]

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?