Senior Data Scientist, Imprint

• 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
• We don't expect every candidate to check every box. If this role excites you and you bring strong fundamentals, we encourage you to apply

Desirable

• Deliver analytical projects that influence product decisions, marketing campaigns, and business strategy, from problem definition through deployment and monitoring
• Build segmentation frameworks and predictive models (churn, LTV, propensity) that drive targeting, personalization, and lifecycle optimization across Imprint's partner programs
• Support A/B testing and experimentation by partnering with Product, Marketing, and Commercial teams to design, analyze, and interpret experiments using scalable frameworks and tooling
• Apply statistical inference, causal analysis, and experimentation design to improve LTV/CAC ratios and accelerate feedback loops on business performance
• Design and build agentic workflows and AI powered systems that explore data, generate hypotheses, monitor business metrics, and operationalize decisions
• Translate complex data into clear narratives for leadership, helping shape how the company thinks about growth, partner health, and customer behavior
• Contribute to team excellence through code reviews, knowledge sharing, and process improvements that raise the bar for the broader Data Science team
• Stack: Python and SQL for modeling and analysis. Snowflake for data warehousing. dbt for data transformation. Sigma for dashboarding. AWS infrastructure