Jeffrey Resten

• Serve as data product owner for multiple embedded analytics experiences and internal dashboards in Looker, defining data contracts, semantic layers, and visualization standards
• Analyze user behavior to identify key statistical drivers of retention, engagement, and “power user” status; partner with Product to translate findings into lifecycle strategies and in‑product experiments that improve adoption and retention
• Design scalable data schemas and LookML models spanning policy/claim messaging flows and customer lifecycle, ensuring consistency of definitions and business logic across teams
• Lead roadmapping for data infrastructure upgrades (e.g., source consolidation, new events, semantic normalization) to support growth, improve quality, reduce latency and enable more robust product analytics
• Establish data quality checks and monitoring for critical KPIs (e.g., activation, engagement, retention), working with engineering to resolve root causes in upstream data

Aerial Imagery Product Solutions

• Led R&D and implementation of a roof age CVML data product leveraged in underwriting and pricing, supporting an estimated $16M in premium leakage recovery through improved risk segmentation and rating accuracy
• Owned vendor data sourcing and integration for new imagery‑based attributes: ran precision/recall testing, cost‑benefit analyses, and partnered with engineering and data science teams to integrate curated features into enterprise data assets and downstream models
• Designed and executed a low‑resolution viability study to assess performance degradation of CVML tasks on downsampled imagery, informing data platform/storage strategy and cost trade‑offs
• Partnered with data science teams to understand and work within their codebase, enabling self‑service inference runs for business stakeholders and accelerating the delivery of model outputs into operational workflows

Auto Claims Advanced Analytics

• Built and maintained claims‑focused analytical data sets and models to support severity, fraud, and operational efficiency initiatives across Auto Claims
• Partnered closely with claims operations, actuaries, and product to define data requirements, reconcile differences in definitions, and drive adoption of analytics insights in underwriting and claims strategies