As data science becomes a critical capability for more companies, engineering leaders are finding themselves responsible for enabling data science teams with infrastructure and tooling. Because data science looks similar to software development (they both involve writing code!), many engineering leaders with the best intentions approach this problem with misguided assumptions, and ultimately hamstring or undermine the data science teams they are trying to support. Learn about the common pitfalls data science leaders face, and what they can do to ensure that their teams perform the best, and maximize impact to the business. Some highlights include:
- Why data science should not be treated like engineering.
- The need for data scientists and business stakeholders to collaborate.
- Special hardware to support burst computing.
- How to support data scientists’ needs to experiment with new tools quickly.