Data Scientist

Imprint is reimagining co-branded credit cards & financial products to be smarter, more rewarding, and truly brand-first. We partner with companies like Crate & Barrel, Rakuten, Booking.com, H-E-B, Fetch, and Brooks Brothers to launch modern credit programs that deepen loyalty, unlock savings, and drive growth. Our platform combines advanced payments infrastructure, intelligent underwriting, and seamless UX to help brands offer powerful financial products—without becoming a bank.

Co-branded cards account for over $300 billion in U.S. annual spend—but most are still powered by legacy banks. Imprint is the modern alternative: flexible, tech-forward, and built for today’s consumer. Backed by Kleiner Perkins, Thrive Capital, and Khosla Ventures, we’re building a world-class team to redefine how people pay—and how brands grow. If you want to work fast, solve hard problems, and make a real impact, we’d love to meet you.

Learn more about us on Imprint's Technology blog.

The Data Analytics team at Imprint builds the data foundation that powers smarter, faster decision-making. The team develops infrastructure and analytics systems that support both daily operations and long-term strategy, enabling high-quality insights into customer behavior, product performance, and business growth.

As Data Scientist, you will own end-to-end analytical projects that directly influence product decisions, marketing campaigns, and executive strategy. You will apply rigorous statistical methods, experimentation design, and predictive modeling to improve customer lifetime value, accelerate feedback loops, and drive measurable business outcomes.

This role blends deep technical expertise with strong business partnership. You will work across the organization—collaborating with product, marketing, and commercial teams—to design experiments, build segmentation frameworks, and translate complex data into clear narratives that shape how Imprint grows. Increasingly, that means building not just analyses but AI-powered systems that can autonomously explore data, generate insights, and operationalize decisions.

• Shipped a new model to production that drives a measurable business outcome

• Delivered a meaningful analysis of a complex business problem, beyond simple A/B test reporting

• Fully integrated with the Data Science team through active participation in code reviews, technical discussions, and knowledge sharing

• Built strong working relationships with key stakeholders and aligned on priorities with your manager and cross-functional partners

• Demonstrated fluency with Imprint's business model, data systems, and user personas—able to explain how the company generates revenue, which partnerships are healthiest, and how your work drives impact

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  Apply statistical inference, causal analysis, and experimentation design to improve LTV/CAC and accelerate feedback loops

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  Champion A/B testing by partnering with cross-functional teams to design, analyze, and interpret experiments rigorously, using scalable frameworks and tooling

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  Build segmentation frameworks and predictive models (churn, LTV, propensity, etc) to drive targeting, personalization, and lifecycle optimization

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  Design and build agentic workflows to automate the data science lifecycle (exploration, modeling, experimentation)

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  Use LLMs and AI tools as collaborators to reason about data, generate hypotheses, and iterate on analyses

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  Build AI-driven systems for monitoring, diagnosing, and automating business insights and decisions

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  Translate data into clear narratives that influence product decisions, marketing campaigns, and executive strategy

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  Support automation projects as needed, including anomaly detection, partner data reporting, and internal self-serve tools or dashboards

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  Own projects end-to-end - from problem definition through implementation, deployment, and monitoring - while collaborating cross-functionally to drive impact

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  Contribute to team excellence through code reviews, technical mentorship, and process improvements

• 6+ years (depending on leveling & education) of experience in data science, analytics, or a related field—ideally at a high-growth startup or fintech company

• Graduate degree in a relevant field (statistics, engineering, science, finance, etc)

• Strong Python and SQL skills, with the ability to transform raw data and build custom datasets when needed

• Highly analytical mindset with a bias toward action and a relentless focus on getting the numbers right

• Ability to clearly communicate complex findings to technical and non-technical audiences

• Comfort owning projects end-to-end and collaborating cross-functionally to drive impact

• Full-stack problem-solving orientation—eager to dive into messy data, test and validate assumptions, and question everything in pursuit of a solution

• Experience building or scaling experimentation infrastructure

• Experience building or improving ML infra

• Familiarity with dashboarding tools such as Sigma or Looker

• Experience in credit, lending, or card products

• Exposure to lifecycle marketing or prescreen modeling

• Background in time series analysis, forecasting, optimization, or simulation

• This is a hybrid role requiring 2–3 days per week onsite

• Open to candidates based in or willing to relocate to San Francisco or New York City