Senior Data Scientist
$170,000–$200,000 year
RemoteUnited States or New York City, New York, United States
Job Summary
Deliver analytical projects influencing product decisions and business strategy from problem definition through deployment. Build segmentation frameworks and predictive models for churn, LTV, and propensity to drive targeting and lifecycle optimization. Support A/B testing by partnering with Product, Marketing, and Commercial teams to design, analyze, and interpret experiments using scalable frameworks. Apply statistical inference and causal analysis to improve LTV/CAC ratios and accelerate feedback loops. Design agentic workflows and AI-powered systems that explore data, generate hypotheses, and operationalize decisions. Translate complex data into clear narratives for leadership to shape growth and partner health strategies. Contribute to team excellence through code reviews, knowledge sharing, and process improvements.
Required Qualifications
- 4 to 7+ years of experience in data science, analytics, or a related quantitative field, ideally at a high growth startup or fintech company
- Degree in a relevant field (statistics, engineering, science, finance, or similar); graduate degree is a plus
- Strong Python and SQL skills, with the ability to transform raw data, build custom datasets, and ship models to production
- Solid foundation in statistical inference, experimentation design, and causal analysis
- Active experience using LLMs and AI tools (Claude, Copilot, Cursor, or similar) as collaborators in your workflow, whether for reasoning about data, generating hypotheses, iterating on analyses, or building agentic automation
- Ability to communicate complex findings clearly to both technical and non technical audiences, including senior leadership and external partner stakeholders
- Full stack problem solving orientation: you dive into messy data, test and validate assumptions, and question everything in pursuit of the right answer
- Comfort owning projects end to end in a fast moving startup environment, collaborating cross functionally with Product, Marketing, Commercial, and Engineering to drive measurable impact
Desired Qualifications
- Experience in credit, lending, or card products
- Experience building or contributing to experimentation infrastructure or ML infrastructure
- Exposure to lifecycle marketing, prescreen modeling, or customer segmentation at scale
- Background in time series analysis, forecasting, optimization, or simulation
- Familiarity with dashboarding tools such as Sigma or Looker
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