Data Scientist, Experimental Projects

Details

Stripe is a financial infrastructure platform for businesses. The Experimental Projects team tests new product opportunities by building prototypes, talking with users, analyzing results, and iterating rapidly. The team works across a broad range of problem spaces and focuses on zero-to-one opportunities.

The Data Scientist will help determine whether new ideas solve meaningful user problems and can become valuable Stripe products. The role involves moving from ambiguous questions to practical tests and working closely with product managers, engineers, designers, finance, and other cross-functional partners.

Responsibilities

• 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 to 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 multiple new problem areas, adapting approaches as priorities and evidence change.
• Communicate findings clearly, including uncertainty, tradeoffs, and recommended next steps.
• Establish analytical foundations for projects that may grow into larger product areas.
• Apply data science throughout the discovery and development process, including product analytics, experimentation, statistical modeling, machine learning, causal inference, and rapid prototyping.
• Choose an appropriate level of analytical rigor for each stage and turn findings into recommendations about what to build or test next.

Requirements

• 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 work independently and with cross-disciplinary teams, including engineering and finance.
• Demonstrated ability to manage and deliver multiple projects with a high attention to detail.
• Solid business acumen and experience synthesizing complex analyses into actionable recommendations.
• Experience building relationships with and influencing senior technical leadership.
• A builder's mindset and willingness to question assumptions and conventional wisdom.
• Proficiency with artificial intelligence tools to accelerate model development, analysis, and coding.

Preferred Qualifications

• Strong knowledge and hands-on experience in several of machine learning, statistics, optimization, product analytics, causal inference, and experimentation.
• Experience deploying models in production and adjusting model thresholds to improve performance.
• Experience designing, running, and analyzing complex experiments or using causal inference methods.
• Experience working on ambiguous, zero-to-one problems and turning early evidence into practical decisions.
• A strong bias for action and the ability to identify the fastest credible way to test a hypothesis.
• Comfort moving across different problem spaces and learning unfamiliar domains quickly.
• Experience with distributed tools such as Spark or Hadoop.
• A PhD or MS in a quantitative field such as statistics, engineering, mathematics, economics, quantitative finance, science, or operations research.

Location

San Francisco, California; hybrid with 50% of time in the Oyster Point office. The vacancy metadata also lists Seattle, United States.