Data Science Lead, Banking & Financial Products
StripeThe new standard in online payments
Posted 1 month ago
Generate insights and impact from data
We're working on making the global financial system programmable. This is one of the largest opportunities for impact in the history of computing, on par with the rise of modern operating systems. A very large part of our endeavor is to expand the scope of problems we tackle beyond payments and into the rest of the financial stack.
We’re looking for an experienced, data science leader who can build and inspire a team as well as lead the development of impactful data products and insights to shape our banking and financial products (e.g., credit lending, corporate card, card issuing, etc). If you are excited about building new products on Stripe’s platform, dream about credit and financial modeling, energized by designing strategic metrics and causal analyses, then we want to hear from you.
- Partner closely with product, engineering, and credit risk teams to develop innovative forecasting, credit, and risk models, leveraging Stripe’s proprietary data, to support new financial products services
- Work with your team to design strategic metrics for our new products and identify causal impacts (through experimentation or causal inference methods).
- Optimize our financial products through machine learning, mathematical optimization, or experimentation
- Shape and influence our data models and instrumentation to generate insights and develop new data products and models
- Lead, mentor, and grow managers, data scientists, and data analysts, creating a healthy and high-performing team
- Recruit great data scientists and data analysts, in collaboration with Stripe’s recruiting team
- Contribute to Data Science org-wide initiatives as part of the Data Science management team
You’d ideally have:
- 7+ years of data science experience, including at least 4+ years of management experience
- A PhD or MS in a quantitative field (e.g., Statistics, Economics, Sciences, Engineering)
- Expert knowledge of a scientific computing language (such as R or Python) and SQL
- Expertise in statistics and experimental design
- Ability to communicate results clearly, including with senior executives
- Successfully built a team, defined its mission, and executed on its goals