Teams at Arlo

Own and improve Arlo’s healthcare underwriting model using large-scale claims data. Develop evaluation frameworks, engineer predictive features, test machine learning architectures, and validate model improvements through backtesting focused on medical loss ratio and pricing competitiveness. Monitor predictions and policy outcomes, identify bias and risk-estimation gaps, and partner with actuaries and ML engineers to deploy reliable models at scale.

Build and own ML infrastructure for underwriting: scalable training on tens of millions of patients, low-latency real-time inference for quoting, and tooling for faster data-science iteration including backtesting, validation, and experiment infrastructure. Ensure reliability, monitoring, SLAs, and on-call operations while collaborating with data scientists and actuaries.

Lead end-to-end data and ML infrastructure powering underwriting and enterprise data ontology. Own ingestion, large-scale model training, low-latency inference, backtesting, monitoring, and API serving. Manage and technical-lead a data engineering and data science team, partner with actuarial and product teams, and improve iteration speed and data quality across healthcare claims use cases.