Geico

Staff Data Scientist, Product

Geico$115K — $230K *
Finance & Insurance
8 - 10 years of experience
Job Overview by Ladders

Qualifications

  • 8+ years in product analytics, decision science, or data science at a tech company.
  • Strong background in designing A/B tests and diagnosing related issues.
  • Solid statistics foundation with practical causal methods experience.
  • Advanced SQL and proficient in Python or R for analytics and modeling.
  • Ability to define product metric frameworks independently.
  • Clear communication skills with effective data storytelling capabilities.
  • Strong quality mindset, focused on quantifying knowledge accurately.

Responsibilities

  • Partner strategically with product, engineering, and design teams to frame business questions.
  • Define and establish goal metrics and supporting metric frameworks for products.
  • Design, conduct, and analyze A/B tests and quasi-experiments with depth.
  • Apply advanced causal methods when randomization is impractical.
  • Develop decision models for opportunity sizing and trade-off analyses.
  • Lead investigations into user behaviors and performance metrics, translating data into actionable insights.
  • Communicate findings to senior leadership effectively and mentor junior data scientists.

Benefits

  • Mentorship opportunities for junior team members.
  • Possibility to influence product decisions at a senior level.
  • Access to advanced analytical tools and datasets.
  • Dynamic work environment with collaborative partners.
  • Potential for contributions to internal analytics frameworks.
Full Job Description
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 leaders-using 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. You'll make critical recommendations for Product, Engineering, and senior leadership.

Job Responsibilities:
Strategic Partnership: Embed with product, engineering, and design leaders as a decision partner-framing 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-offs-pricing, 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 engineers-translate 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 data-can 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 don't, and resisting false precision.
Education: Bachelor's degree or higher in statistics, economics, computer science, mathematics, operations research, or a related quantitative field-or 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.

Annual Salary
$115,000.00 - $230,000.00
The above annual salary range is a general guideline. Multiple factors are taken into consideration to arrive at the final hourly rate/ annual salary to be offered to the selected candidate. Factors include, but are not limited to, the scope and responsibilities of the role, the selected candidate's work experience, education and training, the work location as well as market and business considerations.

GEICO will consider sponsoring a new qualified applicant for employment authorization for this position.

About Geico

GEICO (Government Employees Insurance Company) is an American auto insurance company with headquarters in Chevy Chase, Maryland. It is the second largest auto insurer in the United States, after State Farm. GEICO is a wholly owned subsidiary of Berkshire Hathaway that provides coverage for more than 24 million motor vehicles owned by more than 15 million policy holders as of 2017. GEICO writes private passenger automobile insurance in all 50 U.S. states and the District of Columbia. The insurance agency sells policies through local agents, called GEICO Field Representatives, and over the phone directly to the consumer, and through their website.
Learn more about Geico
Size
40,000 employees
Industry
Founded
1936

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