Senior Data Scientist

Most of what makes American healthcare expensive isn’t medical care. It’s the machinery wrapped around it: middlemen taking a cut, fraud nobody stops, and billing systems designed to fight over payment instead of deliver care. The result is higher premiums, denied claims, surprise bills, and a system patients increasingly experience as adversarial.

Arlo is rebuilding health insurance for small businesses from first principles: making sure as much of every premium dollar as possible goes to care instead of getting absorbed by the system around it. We do that by identifying fraud earlier, steering members toward higher-quality and lower-cost care, automating operational overhead, and eliminating vendors whose business exists mostly to take a cut.

AI is the foundation that makes this work. We use it across underwriting, operations, clinical programs, and member experience to build an insurer that becomes more efficient as the technology improves.

We’re already operating at meaningful scale: profitable, hundreds of millions in premiums, tens of thousands of members covered, and growing quickly through brokers, employers, and partners. Backed by Upfront Ventures, 8VC, and General Catalyst, with a team from Palantir, YC companies, and longtime healthcare operators.

About the role:

Underwriting is at the core of Arlo. Every group we price depends on how accurately we can estimate the risk of the individual members inside, and the quality of the estimate is essential to the sustainability of our business. We’re hiring a Senior Data Scientist to own our underwriting model and continuously deploy measurable improvements to it based on learnings from real-world outcomes. You’ll work with billions of claims across tens of millions of patients to identify what signals in claims history predict future medical cost, how to roll it up to a competitive price for a group, and how to deploy the system at scale. You’ll constantly monitor the lifecycle of predictions, group policies sold, and claims incurred by our tens of thousands of members to gather novel insights that can improve our model and pricing approach.

This is a hands-on modeling role in which you will sit on the underwriting team alongside our team of data scientists and actuaries thinking through issues beyond point estimates of cost including how to handle data blindness, variability, and when the risk is too high to issue a quote. You’ll own the model but work alongside ML engineers to ensure that your ideas can be tested and deployed at scale.

What you’ll work on:

Evaluate the existing Arlo underwriting model and find where it breaks

• Develop a robust evaluation framework to stress the model outcomes and identify specific gaps in risk estimates (cohorts, conditions, or claims patterns) and the underlying causal factors