Head of Data & Machine Learning

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.