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Arlo is rebuilding health insurance for small businesses by redirecting premium dollars toward actual care rather than administrative overhead and middlemen. The company uses AI across underwriting, operations, clinical programs, and member experience to build a more efficient insurer. Already operating at meaningful scale with hundreds of millions in premiums, tens of thousands of members, and backing from top-tier VCs (Upfront Ventures, 8VC, General Catalyst).
As Head of Data & Machine Learning, you will own Arlo's most critical engineering infrastructure: the underwriting system that prices risk and drives growth and profitability. You'll manage a team of six data engineers and data scientists while serving as technical leader for the broader data science organization.
Key responsibilities include:
Underwriting System: Own the complete data pipeline end-to-end—data ingestion, model training and inference, and serving results via API to the quoting frontend. Work closely with the Head Actuary to translate business priorities into executable work. Drive continuous improvement by monitoring model drift, building evaluation infrastructure, and ensuring accuracy as the book of business grows. Improve iteration speed so the team can test, adjust, and deploy faster.
Enterprise Data: Build and maintain Arlo's core data ontology, integrating data from across the organization into a clean, well-governed layer serving underwriting, care management, care navigation, and claims adjudication. Ingest data from multiple sources and build monitoring systems to maintain high data quality.
Technical Leadership: Directly manage the data team, provide code review and architectural guidance, and establish engineering standards and collaboration practices.
You've designed enterprise-wide data architecture and systems that deploy ML models in production. You understand healthcare data nuances including medical claims, diagnosis codes, and procedure codes. You write Python, configure clusters, and stay close to the work. You balance strong engineering standards with business needs and communicate clearly with actuarial and business teams.
