Data Science Manager, Technology

Build out rigorous observability and monitoring for the major systems that power Stripe.

At Stripe, data science managers grow teams and inspire them to rigorous work that shapes our decisions and products. We’re looking for an experienced data science manager to lead our team supporting the Technology Org focusing on Reliability, Efficiency, and Security.

The reliability and security of our systems are essential to ensure that our users can confidently depend on Stripe to grow and run their businesses.  We help Stripe scale its technology predictably, efficiently, and securely while ensuring that availability is high and any problems are detected quickly. If you are naturally data curious, excited about applying science techniques to new areas within Stripe, interested in building anomaly detection systems from the ground up, and motivated by having impact on the business, we want to hear from you. We need your help in building out a culture of rigorous measurement, detection, and optimization.

You will:

• Lead a team of data scientists and analysts to:
• Define and measure key outcome metrics for our systems
• Apply statistical methods, causal inference, experimental design, and predictive modeling to inform technology decisions and optimize our products and systems
• Work closely with our Security, Reliability, and Efficiency teams to build out rigorous anomaly detection, threat detection, and cost forecasting + optimization systems
• Partner closely with engineering teams to identify and prioritize the most important data science projects
• Recruit great data scientists and analysts, in collaboration with Stripe’s recruiting team
• Develop data scientists and analysts on the team, helping them advance in their careers, providing them with continuous feedback

We're looking for someone who has:

• 5+ years of data science experience; 2+ years of management experience 
• A PhD or MS in a quantitative field (e.g., Statistics, Economics, Sciences, Mathematics, Engineering)
• Expert knowledge of a scientific computing language (such as R or Python) and SQL
• Strong knowledge of statistics and experimental design
• Ability to communicate results clearly and a focus on driving impact