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Stash is democratizing wealth creation through education, advice, and financial products. We're seeking a Senior Data Scientist (Technical Level 4) to join our Data team as a strategic partner to Product, Growth, and Marketing.
You'll own high-impact analytical workstreams end-to-end: define ambiguous business problems, select appropriate statistical and ML methods, ship trustworthy results, and influence decisions with clear recommendations. This is not a reporting role—you'll drive measurement and insights that directly improve customer acquisition, activation, retention, and financial advisory outcomes.
Key responsibilities include:
- Own measurement for priority initiatives: Ideal Customer Profile, payback analysis, attribution, subscription performance, and Financial Advice measurement to support company OKRs.
- Design and analyze A/B tests with Product and Marketing, applying statistical rigor and translating results into actionable ship/iterate/kill recommendations.
- Build and productionize predictive and causal models for churn, LTV, conversion propensity, and related outcomes—prioritizing business-measurable, maintainable approaches over experimental science projects.
- Conduct deep-dive analyses of customer and funnel behavior across acquisition, activation, retention, and referral stages; identify drop-offs and size impact before teams invest engineering or media spend.
- Partner with Analytics Engineering to specify data mart fields, definitions, and acceptance criteria; ensure DS work runs on governed, tested warehouse data.
- Create durable analyses, Hex notebooks, and Looker/Mixpanel views that provide lasting leverage; communicate findings clearly to technical and non-technical audiences.
- Raise team standards through methodology review, code review, and contribution to experimentation and documentation practices.
Required qualifications:
- 5+ years in data science or advanced analytics, ideally in consumer tech, fintech, or growth/product analytics; proven Senior-level ownership of ambiguous, multi-quarter problems.
- Strong foundation in experimental design, causal inference, and applied ML (classification, regression, survival/churn, uplift/propensity modeling).
- Proficiency in Python and advanced SQL against large data warehouses.
- Proven ability to partner with PMs, designers, marketers, and engineers; connect analyses to CAC, LTV, retention, ARPU, and commercial outcomes.
- Product sense: comfortable navigating incomplete instrumentation, defining metrics, and pushing for clean event/warehouse contracts.
- Excellent written and verbal communication; can brief executives and coach peers without excessive jargon.
- Bachelor's or Master's in a quantitative field (CS, Statistics, Math, Economics, or equivalent experience).
- Hands-on use of AI coding assistants (Cursor, ChatGPT) as part of daily workflow, with strong judgment on output validation and data sensitivity.
Desirable experience includes attribution modeling, incrementality/geo/holdout testing, marketing mix modeling, dbt/dimensional modeling, Looker/Mixpanel/Hex, and fintech/brokerage/banking/subscriptions background with regulated-data hygiene.
