Senior Data Scientist

Most of what makes American healthcare expensive isnt medical care. Its 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.

Were 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. Were hiring a Senior Data Scientist to own our underwriting model and continuously deploy measurable improvements to it based on learnings from real-world outcomes. Youll 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. Youll 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. Youll own the model but work alongside ML engineers to ensure that your ideas can be tested and deployed at scale.

What youll 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

• Use those findings to generate a roadmap of model and feature work that is prioritized based on making sure our rates are competitive in the market while ensuring we can remain a profitable business.

Build features that capture the full risk of a member

• Account for training and inference dataset bias to optimize member predictions

• Improve handling of member cost variance in our quoting pipeline

Experiment with model designs

• Implement different ML architectures that balance efficacy generalizability and understanding so we can outperform the market

Prove the lift before it ships

• Work with our backtesting harness to measure the effect of every model change on MLR and competitiveness.

• Set the bar for what better means and hold changes to it so improvements to the model are trustworthy

What were looking for:

• 5 years as a data scientist building predictive models that made it into production

• Deep proficiency in Python and SQL with comfort processing large datasets using Spark and using common modeling packages.

• A track record of owning a problem end-to-end in an ambiguous environment and shipping without being handed a spec

• Direct experience with healthcare data

• Strong instincts for feature engineering and model validation and the judgment of how well results might be able to hold in production

• Interest in the larger business context. This is not a research endeavor but a live model that directly changes our win rates book size and performance.

• Clear communicator who can explain what they did and why it mattered

Nice to Have:

• Familiarity with claims data and their known quirks and biases

• Prior experience working at Series C or earlier start-ups

• Background in underwriting actuarial sciences or risk adjustments

• Experience with ML engineering and infrastructure

Compensation

$170000- $220000 equity and benefits

• High ownership: Youll get real responsibility from day oneour 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: Were moving fast and as we grow your scope will grow with usnew challenges bigger opportunities and rapid career velocity.

• Apply AI to a problem that matters: Instead of optimizing ads or cutting labor costs youll 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.

Your safety matters to us. If youre selected to move forward in our hiring process youll hear directly from a member of our Recruiting team via an @ email address. We will never ask for personal or financial information outside of our formal onboarding process. When in doubt please reach out to us to verify at: .

Required Experience:

Senior IC