Head of Data & Machine Learning

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.

You’ve designed enterprise wide data architecture and systems that deploy ML models in production. You care about injecting data into operational workflows and powering the core of a company’s business and not being an ancillary function. You understand the importance of a clean data model. You write Python, configure clusters, and stay close to the work rather than delegating the hard calls away.

You have worked with health care data before and understand the nuances of medical claims, diagnosis codes, procedure codes, etc,

We appreciate strong opinions loosely held and we are looking for someone who can balance good engineering standards with the right business needs. Clear communication skills are important to be able to coordinate with the actuarial team and other business units, understand their requirements and partner closely with the teams who will be the users of your work.

Underwriting System

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  Own the data pipelines & system end to end: data ingestion, model training & inference, and serving results via API to our quoting frontend and manage the underlying infrastructure.

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  Work closely with Sean Chin, Head Actuary, to translate business and actuarial priorities into scoped, executable work for the data team.

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  Drive continuous improvement of the underwriting model: monitor for model drift, build evaluation infrastructure, and ensure the system stays accurate as Arlo’s book of business grows.

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  Improve iteration speed across the underwriting pipeline so the team can test, adjust, and deploy faster.

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  Hold the technical bar across the data function: set engineering standards and establish clear practices for how the team collaborates, documents, and ships.

Enterprise Data

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  Build and maintain Arlo’s core data ontology — integrate data from across the organization into a clean, well-governed layer that can serve use cases including underwriting, care management, care navigation, claims adjudication, etc.

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  Ingest data from multiple sources and build the monitoring systems that keep data quality high.

Technical Leadership & Team

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  Directly manage a team of six; serve as technical lead for the data science team — providing code review, architectural guidance, and the standard they build toward.