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Imprint is a fintech company modernizing co-branded credit card programs for major brands like Crate & Barrel, Rakuten, Booking.com, H-E-B, and Shell. The company combines payments infrastructure, intelligent underwriting, and customer data to create personalized financial experiences without requiring partners to become banks. Backed by top-tier VCs (Kleiner Perkins, Thrive Capital, Ribbit, Khosla Ventures), Imprint is addressing a $300B+ market dominated by legacy systems.
As Senior Data Scientist, you will own analytical projects end-to-end that directly influence product decisions, marketing campaigns, and business strategy. Key responsibilities include building segmentation frameworks and predictive models (churn, LTV, propensity) to drive targeting and lifecycle optimization across partner programs; supporting A/B testing and experimentation by designing and analyzing tests with scalable frameworks; applying statistical inference and causal analysis to improve LTV/CAC ratios; and designing agentic AI workflows that explore data, generate hypotheses, and operationalize decisions. You'll translate complex findings into clear narratives for leadership and contribute to team excellence through code reviews and process improvements.
Required qualifications: 4-7+ years in data science, analytics, or quantitative fields (ideally at high-growth startups or fintech); degree in statistics, engineering, science, finance, or related field; strong Python and SQL for data transformation and production model deployment; solid foundation in statistical inference, experimentation design, and causal analysis; active hands-on experience with LLMs and AI tools (Claude, Copilot, Cursor) as collaborators; ability to communicate complex findings to technical and non-technical audiences including senior leadership and external partners; full-stack problem-solving orientation; and comfort owning projects end-to-end in a fast-moving startup environment.
Nice-to-have skills include experience in credit/lending/card products, experimentation or ML infrastructure, lifecycle marketing, prescreen modeling, customer segmentation at scale, time series analysis, forecasting, optimization, or dashboarding tools like Sigma or Looker.
Tech stack: Python, SQL, Snowflake, dbt, Sigma, AWS.
