Staff Data Scientist, Lead Generation

About the role

Modeling work on carrier matching and paid media bidding, where conversion data arrives late and bids change the data the model learns from next. An ML engineer handles the production side alongside.

At a glance

What you will do

• Build models that match each auto insurance shopper to the carrier most likely to convert
• Own the buying models behind media spend, held to ROAS targets
• Put carrier conversion data (quotes, policies, acquisition cost) into production models
• Handle late labels, sparse sessions, repeat shoppers and market drift
• Take automated marketplace bidding from proof of concept to scale

What they look for

• Five or more years of applied data science with measurable business results
• Models that drove paid media decisions on spend, bidding or ad serving
• Ability to explain your own work down to loss function and architecture, neural network basics included
• Awareness of how bids shape future training data, plus experience with late labels, sparse features or drift
• End to end ownership from finding data through deployment and monitoring
• Expert Python and SQL

Nice to have

• Real ad budget spent in live auctions
• Large-scale ranking or recommendation systems

A typical week

• Framing problems directly with the business
• Finding and preparing your own data
• Building, validating and monitoring models
• Working with an ML engineer on production deployment