Data Scientist: ML, Causal Inference & Impact (Remote)

Stripe; United States; Hybrid; USD 159,000 - 288,000; Full time

Benefits offered by this job

Equity

Company bonus

401(k) plan

Medical benefits

Job summary

Stripe is seeking a Data Scientist to partner with product, finance, payments, security, risk, growth, and go-to-market teams. You will build machine learning and statistical models, conduct experiments, and provide data-driven insights to optimize Stripe’s systems and business decisions.

You will work with cross-functional partners to translate complex analyses into actionable recommendations while deploying and evaluating models.

Qualifications

• PhD/MS/BS in a quantitative field with data science or modeling experience.
• Proficiency in SQL and Python or R for analyses and modeling.
• Experience collaborating with cross-functional teams to deliver results.
• Ability to communicate results clearly and drive impact.
• Experience managing multiple projects with attention to detail.
• Strong business acumen and ability to translate analyses into recommendations.
• Familiarity with AI tools to accelerate model development.

Responsibilities

• Partner with Product, Finance, Payments, Security, Risk, Growth, and GTM teams.
• Build ML and statistical models and run experiments to drive impact.
• Translate analyses into actionable recommendations for business decisions.

Skills

SQL

Python/R

Cross-functional teamwork

Communication

Project management

Business acumen

AI tools

Education

PhD in quantitative field

MS in quantitative field

BS in quantitative field

Tools

Spark

Hadoop

Job description

Stripe is seeking a Data Scientist to partner with product, finance, payments, security, risk, growth, and go-to-market teams. You will build machine learning and statistical models, conduct experiments, and provide data-driven insights to optimize Stripe’s systems and business decisions.

You will work with cross-functional partners to translate complex analyses into actionable recommendations while deploying and evaluating models.