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.

The Opportunity
As Head of Data, you own Arlo’s most critical engineering infrastructure: the underwriting system that prices our risk and drives our growth and profitability. The core of the job is iterating on the underlying model and business logic quickly — testing new approaches and reacting to market shifts like GLP-1 drugs or emerging cancer treatments.

Our underwriting system sits on top of a multi-billion-row claims database. It allows for efficient training of large scale machine learning models, but it also has to serve inference results with low latency. You’ll own this system end to end: data ingestion, model iteration, backtesting, and serving results via API to our quoting frontend. You’ll work closely with Sean Chin, our Head Actuary, to translate business and modelling priorities into what the data team builds next. You will make architectural decisions and be the technical leader for the data engineering and data science team.

Data sits at the core of everything we do — unsurprising for a company founded by an ex-Palantir engineer. We use it to surface care gaps and trigger member outreach, identify fraudulent billing, develop cost-containment strategies, and evaluate doctor quality so our members find the best care. You’ll own the enterprise-wide data layer that feeds all of these operational teams.

In the era of AI, a strong data foundation and well-designed ontology are what make agent deployment actually work, and you’ll lead the organization that builds them — leveraging our existing engineers and making additional hires over the next 12 months.

About You

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.

Responsibilities

Underwriting System

• 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.

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

• 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.

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

• 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

• 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.

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

Technical Leadership & Team

• 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.

• High ownership: You’ll get real responsibility from day one—our high-trust team empowers you to run with big problems and shape core parts of the company.

• Join an important mission: Your work directly influences how people access care and improves lives at scale.

• Growth & expansion: We’re moving fast, and as we grow, your scope will grow with us—new challenges, bigger opportunities, and rapid career velocity.

• Apply AI to a problem that matters: Instead of optimizing ads or cutting labor costs, you’ll use AI to fundamentally reimagine how people get healthcare.

• High pace, high collaboration: We operate with velocity, first-principles thinking, and a team that works closely, openly, and with ambition.

Exact compensation inclusive of salary and any bonuses is determined based on a number of factors including experience and skill level, location, and qualifications which are assessed during the interview process.
Arlo is an equal opportunity employer. We do not discriminate based on age, race, color, creed or religion, national origin, sexual orientation, gender identity or expression, military status, sex, disability, predisposing genetic characteristics, marital status, familial status, status as a victim of domestic violence, or arrest or conviction record, as defined under New York State law.
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