Staff Data Scientist, Product

GEICO is looking for a Staff Data Scientist Product Analytics who will provide quantitative rigor behavioral insight and a strategic perspective to partners across the organization. As a curious and decision-oriented member of the team you serve as the analytical thought partner to product engineering and design leadersusing data experimentation and causal reasoning to help them make better decisions. You will frame the right questions design the studies that answer them and translate findings into recommendations that shape what we build. Youll make critical recommendations for Product Engineering and senior leadership.

Job Responsibilities:
Strategic Partnership: Embed with product engineering and design leaders as a decision partnerframing ambiguous business questions pressure-testing roadmap assumptions and shaping bets before they are committed.
Metric Frameworks: Define goal metrics guardrails and supporting metric trees for your product area. Decompose top-line outcomes into measurable inputs that teams can move.
Experimentation: Design power and analyze A/B and quasi-experiments. Go beyond average treatment effects to understand heterogeneity long-term impact novelty effects and cross-surface interactions.
Causal Inference: Apply causal methods (difference-in-differences synthetic control instrumental variables propensity scoring switchback designs) where randomization is not feasible.
Decision Modeling: Build opportunity sizing forecasting and ROI models that scale how the organization makes trade-offspricing growth retention and long-range investment decisions.
Deep Dives: Lead root-cause investigations into user behavior funnel performance retention and engagement. Translate messy signal into clear defensible recommendations.
Collaboration: Serve as trusted analytics partner to PMs designers and engineerstranslate product questions into research designs analyses and deliverables.
Communication: Present findings and recommendations to senior leadership with clarity structure and its uncertainty.
Mentorship & Best Practices: Mentor junior data scientists and analysts. Define internal standards for experiment design statistical rigor and reproducible analysis.

Basic Qualifications:
Experience: 8 years in product analytics decision science data science or a closely related quantitative role at a technology company with a track record of influencing product decisions.
Experimentation: Strong background designing well-powered A/B tests diagnosing bias and variance issues handling interference and non-stationarity and interpreting results under real-world conditions.
Statistics & Causal Inference: Solid applied statistics foundation with practical experience selecting the right causal method for the question at hand.
Tools: Advanced SQL and working proficiency in Python or R for analysis modeling and reproducible workflows.
Product Sense: Demonstrated ability to define metric frameworks for a product area not just operate within frameworks built by others.
Communication: Clear writing and direct storytelling with datacan produce a one-pager that moves a roadmap and present to executives without losing the nuance.
Quality Mindset: Disciplined about quantifying what you know surfacing what you dont and resisting false precision.
Education: Bachelors degree or higher in statistics economics computer science mathematics operations research or a related quantitative fieldor equivalent practical experience.

Preferred Qualifications:
Advanced Methods: Bayesian methods hierarchical models sequential testing or uplift modeling experience.
Domain Experience: Background in growth pricing monetization marketplace or recommendation problem spaces.
ML Partnership: Experience working alongside ML engineers on production models including offline evaluation online metrics and guardrails.
Platform Contributions: Contributions to internal experimentation platforms metric stores or measurement tooling.
Education: MS or PhD in a quantitative discipline.
Industry: Insurance or financial services experience.