Senior Data Scientist
Stash
New York
Data
Job Score
80 pts
On-site model (+70)
Data (+10)
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.
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.
#LI-Hybrid
Our Commitment to Diversity, Equity, and Inclusion
We proudly celebrate the unique qualities that make you you, 365 days a year, and not just because it’s the right thing to do or good for business. We embed the principles and practices of diversity, equity, and inclusion (DEI) into all that we do to prioritize people, a Stash core value, and to ensure Stashers of all backgrounds and experiences can be their authentic selves.
We are also proud to be the first and only venture-backed fintech to join the CEO Action for Diversity & Inclusion™, and as an Equal Opportunity Employer, Stash is committed to building an inclusive environment for people of all backgrounds.
If you require any reasonable accommodations to make your application process more accessible, please reach out to recruiting@Stash.com.
Helping You Invest in Yourself
- Comprehensive total rewards package, comprising compensation (salary and equity) and health care benefits
- Complimentary subscription to Stash+ account
- Flexible work policy – We offer a flexible work environment that blends working from home with in-person collaboration at our NYC office to support productivity and team culture.
- Flexible PTO
- Annual learning and development reimbursement benefit
- Work-from-home equipment stipends; home internet subsidy
- Paid Parental Leave (offerings for birth giving and non-birth giving parents) Primary & Secondary
- Enhanced health and wellness benefits through One Medical, Gympass, and Maven Health
External Recognition for Stash
- Benzinga’s 2023 Best Brokerage for Beginners and Best Robo-Advisor Awards
- Qorus-Accenture’s 2023 Banking Innovation Awards
- USA Today and Statista’s 2023 Top 500 Best Financial Advisory Firms
- Comparably's Best Company Awards: Best Places to Work, Best Company Outlook, and Best Engineering Team for Diversity, Women, Culture, and more! (2023)
- Fintech Breakthrough Award: Best Personal Finance App (2023)
- BuiltIn’s Best Places to Work (2022, 2021, 2020, 2019)
- Forbes Fintech 50 (2021, 2020, 2019)
- Best Digital Bank, Finovate Awards (2020)
- Tearsheet Challenge Awards, Best Banking Card Product - Stock-Back® Card, 2020
- LendIt Fintech Innovator of the Year (2020, 2019)
Salary Range: $150,000 - $180,000
The base salary range represents the reasonably anticipated low and high end of the salary range for this position. Actual salaries will vary and will be based on various factors, such as the candidate’s qualifications, skills, experience and competencies, as well as internal equity and alignment with market data for companies of our size and industry.
**No recruiters, please**
About Data
The Data field has undergone a radical transformation with the rise of Generative AI. Data professionals are fundamental for evidence-based decision-making across all industries.
Key specializations include Data Engineering, Data Science, Business Intelligence, Machine Learning Engineering, and Analytics. Tools like SQL, Python, Spark, dbt, and cloud platforms (AWS, GCP, Azure) are essential.
The data market continues with high demand and salaries among the most competitive in the technology sector, with many remote work opportunities.
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