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About This Role
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 [email protected].
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\*\*
Salary Context
This $150K-$180K range is above the median for Data Scientist roles in our dataset (median: $160K across 258 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Stash, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($165K) sits 14% below the category median. Disclosed range: $150K to $180K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Stash AI Hiring
Stash has 1 open AI role right now. They're hiring across Data Scientist. Based in New York, NY, US. Compensation range: $180K - $180K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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