This role pays more than
89%
of similar roles. Most pay
$126,800–$208,918
— the shaded band above.
At the midpoint, this role pays about
$240k
versus about
$168k
for comparable roles.
Based on 240 similar postings.
Employer
About Stripe
Stripe is a financial infrastructure platform for internet businesses, providing payment processing, billing, fraud prevention, and banking-as-a-service APIs to businesses of all sizes globally. Industry: Payments Infrastructure & Financial Technology
Stripe currently has
176 open roles
on FindRole.
Listed pay typically runs
$198,600–$291,650
across 174 roles with salary data.
As a Data Scientist, Experimental Projects, you will join the Experimental Projects team to test new product opportunities by tackling zero-to-one problems. You will partner with engineers and product managers to build prototypes, develop models, and conduct experiments that help determine if new ideas can solve meaningful user problems. Your daily work involves using data to shape hypotheses, combining qualitative insights from user conversations with quantitative evidence, and defining success measures for early concepts. The role requires proficiency in SQL, Python or R, and artificial intelligence tools to accelerate development. You will apply techniques such as product analytics, machine learning, causal inference, and statistical modeling to navigate ambiguous problems. By providing clear recommendations on trade-offs and next steps, you will establish the analytical foundations for projects that may eventually evolve into larger product areas.
What does a Data Scientist earn in California?
Median $214000 from 74 postings across 27 companies.
Use data to identify, evaluate, and shape new product opportunities.
Build and test early product concepts in collaboration with engineers and product managers.
Develop analyses, models, experiments, and prototypes to facilitate rapid learning.
Combine qualitative user insights with quantitative evidence to inform product decisions.
Define success measures for new ideas and assess results to determine investment viability.
Apply various methodologies including machine learning, causal inference, and experimentation to solve problems.
Communicate findings clearly by highlighting uncertainties, tradeoffs, and recommended next steps.
Establish analytical foundations for projects that may evolve into larger product areas.
What we're looking for
PhD with 3+ years, MS/MA with 6+ years, or BS/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 like engineering and finance to deliver results.
Demonstrated ability to manage multiple projects with high attention to detail.
Solid business acumen and experience synthesizing complex analyses into actionable recommendations.
Track record of building relationships with and influencing the decisions of senior technical leadership.
Proficiency with artificial intelligence tools to accelerate model development, analysis, and coding.
Experience in machine learning, statistics, optimization, product analytics, causal inference, or experimentation (preferred); PhD/MS in a quantitative field (preferred).
Python
SQL
NoSQL
Data Visualization
Information Retrieval
Statistical Modeling
Time-Series Analysis
Anomaly Detection
Exploratory Data Analysis
Multi-modal Data