Senior Data Scientist

The role

You will own the underwriting model and deploy improvements based on real-world outcomes. You will work with billions of claims to identify signals that predict future medical costs and deploy the system at scale.

What you will do

• Evaluate the existing Arlo underwriting model and find where it breaks
• Develop a robust evaluation framework to stress model outcomes
• Generate a roadmap of model and feature work
• Build features that capture the full risk of a member
• Account for training and inference dataset bias
• Improve handling of member cost variance in the quoting pipeline
• Implement different ML architectures
• Work with a backtesting harness to measure the effect of model changes
• Set the bar for what better means and hold changes to it

Required qualifications

• 5+ years as a data scientist building predictive models that made it into production
• Deep proficiency in Python and SQL
• 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
• Interest in the larger business context
• 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

Benefits

• Equity
• Benefits

About the company

Arlo is rebuilding health insurance for small businesses from first principles. They use AI across underwriting, operations, clinical programs, and member experience.