Staff Data Scientist owning end-to-end analytical projects that influence product decisions, marketing campaigns, and executive strategy. Applies statistical methods, experimentation design, and AI-powered systems to improve customer lifetime value and business outcomes.
200k – 250k
Hybrid7+ YOEData Science
About the role
Responsibilities
Apply statistical inference, causal analysis, and experimentation design to improve LTV/CAC and accelerate feedback loops
Champion A/B testing by partnering with cross-functional teams to design, analyze, and interpret experiments rigorously, using scalable frameworks and tooling
Build segmentation frameworks and predictive models (churn, LTV, propensity, etc) to drive targeting, personalization, and lifecycle optimization
Design and build agentic workflows to automate the data science lifecycle (exploration, modeling, experimentation)
Use LLMs and AI tools as collaborators to reason about data, generate hypotheses, and iterate on analyses
Build AI-driven systems for monitoring, diagnosing, and automating business insights and decisions
Translate data into clear narratives that influence product decisions, marketing campaigns, and executive strategy
Support automation projects as needed, including anomaly detection, partner data reporting, and internal self-serve tools or dashboards
Own projects end-to-end - from problem definition through implementation, deployment, and monitoring - while collaborating cross-functionally to drive impact
Contribute to team excellence through code reviews, technical mentorship, and process improvements
Requirements
7-12+ years 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
Nice to Have
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
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