Staff Data Scientist

Apply statistical inference, causal analysis, and experimentation design to improve LTV/CAC and accelerate feedback loops

Champion A/B testing by partnering with cross-functional teams to design, analyze, and interpret experiments rigorously, using scalable frameworks and tooling

Build segmentation frameworks and predictive models (churn, LTV, propensity, etc) to drive targeting, personalization, and lifecycle optimization

Design and build agentic workflows to automate the data science lifecycle (exploration, modeling, experimentation)

Use LLMs and AI tools as collaborators to reason about data, generate hypotheses, and iterate on analyses

Build AI-driven systems for monitoring, diagnosing, and automating business insights and decisions

Translate data into clear narratives that influence product decisions, marketing campaigns, and executive strategy

Support automation projects as needed, including anomaly detection, partner data reporting, and internal self-serve tools or dashboards

Own projects end-to-end - from problem definition through implementation, deployment, and monitoring - while collaborating cross-functionally to drive impact

Contribute to team excellence through code reviews, technical mentorship, and process improvements

7-12+ years (depending on leveling & education) of experience in data science, analytics, or a related field—ideally at a high-growth startup or fintech company

Graduate degree in a relevant field (statistics, engineering, science, finance, etc)

Strong Python and SQL skills, with the ability to transform raw data and build custom datasets when needed

Highly analytical mindset with a bias toward action and a relentless focus on getting the numbers right

Ability to clearly communicate complex findings to technical and non-technical audiences

Comfort owning projects end-to-end and collaborating cross-functionally to drive impact

Full-stack problem-solving orientation—eager to dive into messy data, test and validate assumptions, and question everything in pursuit of a solution

Experience building or scaling experimentation infrastructure

Experience building or improving ML infra

Familiarity with dashboarding tools such as Sigma or Looker

Experience in credit, lending, or card products

Exposure to lifecycle marketing or prescreen modeling

Background in time series analysis, forecasting, optimization, or simulation

This is a hybrid role requiring 2–3 days per week onsite

Open to candidates based in or willing to relocate to San Francisco or New York City