Data Science

Telematics

Telematics Data Scientist

As a telematics data scientist, I conduct research on driving data and market trends to guide our business partners on product strategy. On the R&D side, I build predictive models and perform analysis using cloud computing tools — including AWS, Databricks, and Spark. On the business side, I conduct market research to develop product strategies informed by findings from our models and analysis. In practice, the role spans data analysis, data modeling, and product strategy.

Table of Contents

1. Industry History — Thirty years behind the wheel: how telematics grew from a Cadillac gimmick into the force reshaping auto insurance.

2. Driving Exposure — From calendars to sensors: the unfinished science of measuring driving exposure in auto insurance.

Telematics in insurance is a young discipline — barely twenty-five years old — and still finding its shape. When I began studying it in earnest, I took the same approach that had served me well entering biophysics during a similarly unsettled period: start from first principles, read deeply, and let the research set the direction.

That meant tracing the technological foundations — satellite positioning, on-board diagnostics — through the regulatory and market forces that bent them into an industry. From there, the work moved into behavioral science: how drivers actually behave behind the wheel, what the academic literature says about the relationship between behavior and risk, and how traditional actuarial models have tried to approximate driving quality through psychology and claims history. I studied product strategy in parallel — how companies have segmented markets, competed on data, and where the field is running into friction on two fronts — pricing frameworks and risk models that haven't caught up to what modern sensors can capture, and a structural ceiling on environmental data, where the contextual resolution needed to fully interpret driver behavior remains locked behind political, financial, and legal barriers that no single carrier or third party has managed to overcome.

That foundation has had practical consequences. It enabled me to reduce time-to-insight in our data analysis pipeline by ninety percent, and to develop product strategies aimed at driver populations that conventional telematics programs routinely overlook — work that has opened new market segments and allowed us to meaningfully grow our book of business.

What follows is a synthesis of what that study produced: the history of the field, the political and regulatory currents reshaping it, and the next generation of metrics and models I believe will define telematics as it matures — and as it moves closer to letting drivers demonstrate, rather than merely be assigned, their actual risk. The table of contents above serves as a guide to each of these topics, and will grow as new sections are added.