Data Scientist, Experimental Projects

Stripe, Inc.; San Francisco (CA); Hybrid; USD 140,000 - 190,000; Full time

Job summary

United States Digital Space LLC is seeking a Data Scientist to identify and shape new product opportunities using data analysis, experiments, and modeling. You will collaborate with engineers, PMs, and designers to test early concepts and build prototypes while communicating insights to leadership.

The role emphasizes a builder's mindset, cross-functional work, and the ability to turn ambiguous questions into practical tests, with a hybrid SF location requirement and strong focus on rapid

Qualifications

• PhD with 3+ years, MS 6+ years, or BS 8+ years in data science or quantitative modeling.
• Proficiency in SQL and Python or R.
• Ability to work independently and with cross-disciplinary teams.
• Delivery of multiple projects with high attention to detail.
• Strong business acumen; translate analyses into actions.
• Track record influencing senior technical leadership.
• Builder mindset; question assumptions.
• Familiarity with AI tools to accelerate modeling.

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 that help learn quickly.
• Talk with users and combine qualitative insights with quantitative evidence.
• Define success measures for new ideas and assess early results.
• Work across several new problem areas, adapting as priorities and evidence change.
• Communicate findings clearly, including uncertainty, tradeoffs, and next steps.
• Help establish analytical foundations for projects that may grow into larger product areas.

Skills

SQL

Python

R

Cross-functional teamwork

Quantitative modeling

Data-driven decisions

Leadership influence

AI tools familiarity

Education

PhD in quantitative field

MS in quantitative field

BS in quantitative field

Tools

Spark/Hadoop

SQL databases

Experimentation tools

Job description

Who we are

About the company

the company is a financial infrastructure platform for businesses. Millions of companies-from the world's largest enterprises to the most ambitious startups-use the company 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.

About the team

The Experimental Projects team quickly tests new product opportunities for the company. 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 will help determine whether new ideas can solve meaningful user problems and become valuable products for the company. We are 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.

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 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.

What you'll do

You will partner closely with product managers, engineers, designers, and other cross-functional partners to explore new product opportunities. You will 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.

Who you are

We are 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.

Location Requirement

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

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.

• 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.

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 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