Data Scientist, Stripe

• We’re looking for a Data Scientist who enjoys building, has a strong bias for action, and is comfortable moving from an ambiguous question to a practical test
• PhD with 3+ years, MS or MA with 6+ years, or BS or BA with 8+ years of data science or quantitative modeling experience
• Proficiency in SQL and a computing language such as Python or R
• Ability to effectively work both independently and with cross-disciplinary teams, including engineering and finance, to deliver impactful results
• A demonstrated ability to manage and deliver on multiple projects with a high attention to detail
• Solid business acumen and experience in synthesizing complex analyses into actionable recommendations
• A track record of building relationships with and influencing the decisions of senior technical leadership
• A builder's mindset with a willingness to question assumptions and conventional wisdom
• Proficiency with artificial intelligence tools to accelerate model development, analysis, and coding

Desirable

• The Experimental Projects team quickly tests new product opportunities for Stripe. We work on brand-new, zero-to-one problems by building prototypes, talking with users, analyzing what we learn, and iterating rapidly
• The team operates across a broad range of problem spaces. Rather than optimizing a single mature product area, you’ll help determine whether new ideas can solve meaningful user problems and become valuable products for Stripe
• Use data to identify, evaluate, and shape new product opportunities
• Partner with engineers and product managers to build and test early product concepts
• Develop analyses, models, experiments, and prototypes that help the team learn quickly
• Talk with users and combine qualitative insights with quantitative evidence
• Define success measures for new ideas and assess whether early results support further investment
• Work across several new problem areas, adapting your approach as priorities and evidence change
• Communicate findings clearly, including uncertainty, tradeoffs, and recommended next steps
• Help establish analytical foundations for projects that may grow into larger product areas
• You’ll partner closely with product managers, engineers, designers, and other cross-functional partners to explore new product opportunities
• You’ll use data science throughout the discovery and development process, from identifying promising problems and shaping hypotheses to building early solutions and evaluating results
• Your work may include product analytics, experimentation, statistical modeling, machine learning, causal inference, and rapid prototyping
• The specific methods will depend on the opportunity. Success in this role requires choosing the right level of analytical rigor for each stage, working quickly when evidence is limited, and turning what you learn into clear recommendations about what the team should build or test next