Principal Product Manager, Experimentation & Digital Analytics

GEICO, subsidiary of Berkshire Hathaway and a leader in Auto insurance and other product lines, is on a multi-year technology transformation journey to reimagine the customer experience in the Insurance industry by removing friction across customers, partners, marketplace, segments, and channels and building a world-class digital experience powered by modern data and AI. 

As part of this transformation, we are looking for an accomplished, customer-obsessed, results-oriented Principal Product Manager to own our experimentation and digital analytics platforms and drive a company-wide culture of experimentation and data-driven decision making. This role is central to GEICO's ability to learn faster, act on evidence, and deliver greater customer and business impact in service of GEICO's growth and retention objectives. 

As a Principal Product Manager, you will own the tools and platforms that power experimentation, digital behavioral analytics, and insight generation across GEICO. You will define and drive the strategy for these platforms, championing their adoption across product, design, engineering, marketing, and analytics teams, and enabling those teams to self-serve, automate, and scale their own experimentation and analysis. You will be responsible for increasing the velocity, quality, and business value of experimentation, analysis, and insight generation enterprise-wide — while ensuring rigor, statistical integrity, and trust in the results. You will collaborate closely with cross-functional teams, including engineering, design, marketing, and analytics, to deliver high-impact platform capabilities that drive business growth and customer satisfaction. You must be comfortable communicating and influencing at all levels of the organization. 

Additionally, you bring a strong quantitative and statistical foundation combined with prior experience building or leading experimentation programs, and you are energized by turning a nascent or fragmented practice into a scaled, self-service, insight-driven capability. 

You are also an AI-native product manager who uses AI tools daily to move faster — from synthesizing research and generating experiment hypotheses to accelerating analysis and prototyping platform capabilities — and who sees applying AI to automate and scale insight generation itself as core to this role, not a side skill. 

This is a hybrid position, requiring on-site presence 2-3 days a week at one of the following locations:  Palo Alto, CA; Seattle, WA; Bethesda, MD.

Job Responsibilities 

• Own the product vision, strategy, and roadmap for GEICO's experimentation and digital analytics platforms, aligned to GEICO's growth, retention, and digital transformation goals. 

• Drive adoption of experimentation and digital analytics tools across product, design, engineering, marketing, and analytics teams, removing friction and building trust in the platforms and their outputs. 

• Enable teams to self-service and automate experiment design, instrumentation, execution, analysis, and reporting, reducing dependency on manual or ad hoc processes. 

• Define and evolve the standards, guardrails, and governance for experimentation (e.g. statistical methodology, sample size and power, metric definitions, guardrail metrics, peer review) to ensure decisions are grounded in rigorous, causally sound evidence. 

• Partner with data science, engineering, and analytics leaders to define the architecture, instrumentation strategy, and measurement pipelines that underpin experimentation and digital analytics at scale. 

• Lead cross-functional teams through the entire product lifecycle for platform capabilities, from concept to launch and beyond. 

• Conduct research with platform users (product managers, analysts, data scientists, engineers) to identify friction points, unmet needs, and the highest-leverage opportunities to improve velocity and quality of experimentation and insight generation. 

• Prioritize features and initiatives based on user feedback, business impact, and technical feasibility, making trade-off decisions when needed. 

• Drive product development efforts, including defining requirements, managing backlog, and ensuring timely delivery of high-quality releases. 

• Define north-star metrics and KPI trees for platform health and impact (e.g. experiment velocity, coverage, time-to-insight, adoption, decision quality) and continuously monitor and iterate against them. 

• Champion a culture of experimentation and data-driven decision making across GEICO through enablement, training, evangelism, and demonstrated business impact. 

• Collaborate with stakeholders across the organization to build alignment and drive decisions on platform strategy, prioritization, and investment. 

• Identify options and recommendations, working through trade-offs with other leaders to remove impediments for the team. 

• Oversee platform rollout plans, segmentation of user needs across teams, and opportunities to promote adoption and best practices. 

• Partner with Data & Technology leaders to influence end-state architecture and drive secure, resilient, performant, and scalable platform solutions that address material customer and business problems. 

Basic Qualifications: 

• Bachelor's degree required, in a quantitative field (e.g. Statistics, Mathematics, Economics, Computer Science, Engineering) strongly preferred. 

• 10+ years of experience in product management, with significant ownership of platform, tools, or data/analytics products used broadly across an organization. 

• Prior experience building, scaling, or leading an experimentation program or platform (e.g. A/B testing infrastructure, feature flagging, causal measurement) at a company of meaningful scale. 

• Strong analytical and statistical background, with hands-on fluency in experimental design and statistical inference (e.g. hypothesis testing, statistical power, confidence intervals, false discovery/positive control, variance reduction). 

• Proven track record of driving adoption of self-service tools or platforms and measurably increasing the velocity and quality of decision-making across teams. 

• Strong quantitative background with hands-on experience analyzing large datasets and making causally grounded decisions. 

• Strong leadership skills with the ability to influence diverse stakeholders and inspire cross-functional teams without direct authority. 

• Excellent communication and presentation skills, with the ability to effectively articulate complex statistical and technical concepts to both technical and non-technical audiences. 

• Experience working with Agile methodologies and tools such as JIRA or Azure DevOps. 

• Passion for innovation, continuous learning, and driving positive change. 

Preferred Qualifications: 

• Advanced degree (MS or PhD) in Statistics, Economics, Data Science, Computer Science, or a related quantitative field. 

• Direct experience owning or building an experimentation or digital analytics platform (e.g. feature flagging and rollout systems, A/B testing platforms, session replay/behavioral analytics tools, or internal frameworks), whether commercial or homegrown. 

• Deep expertise in causal inference and applied statistics (e.g. power analysis, sequential testing, variance reduction techniques such as CUPED, heterogeneous treatment effects, quasi-experimental methods such as difference-in-differences or synthetic controls). 

• Experience defining and evolving experimentation governance, including metric standards, guardrails, peer review processes, and centers of excellence. 

• Hands-on fluency in SQL, Python, and/or R, and experience defining instrumentation and event schemas for digital analytics. 

• Experience with digital behavioral analytics tools (e.g. clickstream, session replay, funnel and journey analysis) and connecting quantitative insight to qualitative customer understanding. 

• Experience building enablement programs, training curricula, or centers of excellence that scaled a data-driven or experimentation culture across a large organization. 

• Experience in insurance, financial services, or another highly regulated industry. 

• Familiarity with model evaluation, monitoring, and experimentation practices for ML-driven or personalization systems. 

• MBA, MS, or technical degree a plus.