Data Scientist

Details

Stripe's Data Science team partners with teams across the company to provide the models, data products, and insights needed to make decisions and grow responsibly. Data Scientists analyze data, build machine learning and statistical models, and run experiments to drive impact across products, fraud prevention, charge-flow optimization, forecasting, liquidity management, risk exposure, growth, marketing, sales-process optimization, and causal-effect estimation.

Responsibilities

• Partner with the Product, Finance, Payments, Security, Risk, Growth, and Go-to-Market teams.
• Optimize systems and use data to support strategic business decisions.
• Apply machine learning, statistical modeling, causal inference, optimization, experimentation, and analytics.
• Work cross-functionally to deliver results and drive business impact.
• Design, run, and analyze complex experiments and causal-inference studies.
• Deploy models in production and adjust model thresholds to improve performance.
• Manage and deliver multiple projects with a high level of attention to detail.
• Use AI tools to accelerate model development, analysis, and coding.

Requirements

Minimum 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.
• Experience working with cross-functional teams to deliver results.
• Ability to communicate results clearly and focus on driving impact.
• Demonstrated ability to manage and deliver multiple projects with strong attention to detail.
• Strong business acumen and experience synthesizing complex analyses into actionable recommendations.
• Proficiency with AI tools to accelerate model development, analysis, and coding.

Preferred Qualifications

• Strong knowledge and hands-on experience in several of the following areas: 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 leveraging causal-inference designs.
• A builder's mindset and willingness to question assumptions and conventional wisdom.
• Experience with distributed tools such as Spark and Hadoop.
• A PhD or MS in a quantitative field such as Statistics, Engineering, Mathematics, Economics, Quantitative Finance, Sciences, or Operations Research.