Senior Data Scientist

Want to help everyday Americans invest and build wealth? Financial inequality is increasing, and too many people are getting left behind. At Stash, we’re passionate about democratizing wealth creation through education, advice, and products that help customers achieve greater financial freedom. We also believe in working smarter—leveraging AI and emerging technologies to move faster, operate more efficiently, and focus our time on solving meaningful problems for our customers.

We’re looking for a Senior Data Scientist (Technical Level 4) to join our Data team. You’ll be a strategic partner to Product, Growth, and Marketing—turning ambiguous business questions into rigorous measurement, experiments, and models that improve how we acquire, activate, retain, and advise customers.

This is not a pure reporting role. You’ll own high-impact analytical workstreams end-to-end: define the problem, choose the right method, ship trustworthy results, and influence decisions with clear recommendations. If you thrive at the intersection of statistics, product sense, and stakeholder partnership, we’d love to hear from you.

This position operates on a hybrid schedule, requiring you to be on-site in our New York office at least three days per week to work with stakeholders and foster team collaboration.

What you'll do:

• Own measurement for priority bets: Partner with Product and Growth on our Ideal Customer Profile, payback, attribution, subscription performance, and Financial Advice (FA) measurement—so leaders can trust the numbers behind company OKRs.
• Design and analyze experiments: Lead A/B testing with Product and Marketing. Apply statistical rigor and translate results into ship / iterate / kill recommendations.
• Build predictive and causal models: Develop and productionize models for churn, LTV, conversion propensity, and related outcomes. Prefer approaches that are measurable in business terms and maintainable in our stack—not science projects that never ship.
• Deep-dive customer and funnel behavior: Analyze acquisition → activation → retention → referrals. Find drop-offs, segment opportunities, and growth levers; size impact before teams invest engineering or media spend.
• Partner on data foundations: Specify grains, definitions, and acceptance criteria for new data mart fields and models; work with Analytics Engineering so DS work runs on governed, tested warehouse data—not one-off SQL that drifts.
• Enable decision-making with clarity: Build durable analyses, Hex notebooks, and Looker / Mixpanel views where they create lasting leverage. Communicate findings to technical and non-technical audiences with crisp narratives and recommended actions.
• Raise the bar for the team: Review methodology and code, and contribute to team standards for experimentation, documentation, and AI-assisted workflows (with judgment on sensitive data).

What we're looking for:

• Experience: 5+ years in data science or advanced analytics roles, ideally in consumer tech, fintech, or growth/product analytics. Prior Senior ownership of ambiguous, multi-quarter problems.
• Statistical & ML craft: Strong foundation in experimental design, causal inference, and applied machine learning (classification/regression, survival/churn, uplift or propensity where relevant). You know when a simple model beats a complex one.
• Programming: Proficiency in Python and advanced SQL against large warehouses.
• Business partnership: Proven ability to work with PMs, designers, marketers, and engineers; connect analyses to CAC, LTV, retention, ARPU, and other commercial outcomes.
• Product sense: Comfortable navigating incomplete instrumentation, defining metrics, and pushing for clean event/warehouse contracts when measurement depends on them.
• Communication: Excellent written and verbal communication; can brief executives and coach peers without drowning either audience in jargon.
• Education: Bachelor’s or Master’s in a quantitative field (CS, Statistics, Math, Economics, or related), or equivalent experience.
• AI fluency: Hands-on use of AI coding assistants (e.g. Cursor, ChatGPT) as part of daily workflow, with strong judgment—validating outputs, following Stash guidelines for sensitive data, and owning the quality of AI-assisted work.

Gold Stars:

• Experience with attribution modeling, incrementality / geo or holdout tests, and marketing mix or media measurement.
• Familiarity with dbt, dimensional modeling, and reading warehouse lineage.
• Experience with Looker, Mixpanel, and/or Hex (or similar BI / product analytics / notebook stacks).
• Fintech, brokerage, banking, or subscriptions experience; comfort with regulated-data hygiene.