Remote Senior Data Scientist, AdTech & Media Buying

launchpotato; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Launch Potato is seeking a senior data scientist to build models that optimize insurance carrier selection for shoppers on our click wall. You will own data from collection to deployment and oversee production in partnership with an ML engineer.

The role is remote-first, with East Coast-aligned hours, and a focus on measurable business impact across high-velocity media campaigns. You will turn sparse, late-label data into robust models, improve bidding decisions, and drive continual improvements

Qualifications

• 5+ years of hands-on applied data science with measurable business impact.
• Models drove paid media decisions: spend, bidding or ad serving. Attribution and reporting alone don't qualify.
• You built what's on your resume and can explain it down to the loss function and architecture choice, neural network fundamentals included
• You see how today's bids shape tomorrow's training data, and you've handled late labels, sparse features or drift
• You own models end to end: you find your own data, build through deployment and monitoring, and bring a point of view
• Expert Python and SQL

Responsibilities

• Develop predictive models to match users with carriers for insurance.
• Own the data pipelines from collection to deployment and monitoring.
• Collaborate with the ML engineer on production-side integration and drift handling.

Skills

Applied data science

Python

SQL

End-to-end modeling

Media analytics

Tools

AWS SageMaker

SQL databases

Job description

Launch Potato is seeking a senior data scientist to build models that optimize insurance carrier selection for shoppers on our click wall. You will own data from collection to deployment and oversee production in partnership with an ML engineer.

The role is remote-first, with East Coast-aligned hours, and a focus on measurable business impact across high-velocity media campaigns. You will turn sparse, late-label data into robust models, improve bidding decisions, and drive continual improvements