Build and own the infrastructure layer that powers our underwriting model, trained on tens of millions of patients and hundreds of millions of rows of claims data.
Make training reliable, reproducible, and scalable as data volume and model complexity grow.
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.
Population Health Strategy: Identify key needs across Arlo’s member populations and develop scalable, data-driven programs to improve outcomes and reduce the total cost of care.
Care Navigation Models: Help guide how Arlo’s concierge and care support services proactively engage members—especially those with complex or rising risk conditions.
