Data Scientist, Experimental Projects

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world's largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career.

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. 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.

• →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.

We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

• San Francisco, CA (Hybrid: 50% in office - Oyster Point)

• 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.

• 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 using causal inference methods
• A builder’s mindset and willingness to question assumptions and conventional wisdom
• Experience working on ambiguous, zero-to-one problems and turning early evidence into practical decisions
• A strong bias for action, including 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