Lead Data Scientist, AdTech

Launch Potato•San Diego, CA

1d•$175,000 - $200,000•Remote

About The Position

Launch Potato is hiring a Lead Data Scientist for a hands-on individual contributor role focused initially on auto insurance. The role builds models that select which carrier each shopper sees on the click wall, with the goal of matching users to carriers most likely to convert. It also owns media-buying models that guide spend and are measured against return on ad spend (ROAS). The role will put carrier conversion data—including quotes, policies, and acquisition cost—into production models.

Requirements

At least 5 years of hands-on applied data science experience with measurable business impact.
Experience building models that drove paid media decisions such as spend, bidding, or ad serving; attribution and reporting alone do not qualify.
Ability to explain the models personally built, including loss-function and architecture choices and neural network fundamentals.
Understanding of how bids influence future training data, with experience handling late labels, sparse features, or drift.
Experience owning models end to end, from finding data and building models through deployment and monitoring.
Expert-level Python and SQL.

Nice To Haves

Experience spending real ad budgets in live auctions.
Experience with large-scale ranking or recommendation systems.
Ability to explain tradeoffs to non-technical partners in plain language.
Experience designing for auction feedback loops, selection bias, and exploration.
Hands-on neural network experience, such as embeddings of text, creative, or user behavior.
AWS data science experience with ECS, S3, Athena, or SageMaker.
CI/CD deployment experience.

Responsibilities

Build and validate models that match auto insurance shoppers with the carrier most likely to convert.
Own the models behind media buying and paid media decisions, with performance measured by ROAS.
Use carrier conversion data, including quotes, policies, and acquisition cost, in production models.
Frame problems directly with business partners and independently find and prepare data.
Own models end to end, including development, deployment, and monitoring, working with an ML engineer on production.
Develop approaches for delayed outcomes, sparse sessions, repeat shoppers, market drift, and feedback effects where bids shape future training data.
Run insurance marketplace media buying independently by day 60.
Take automated marketplace bidding from proof of concept to scale.
Broaden the role's modeling scope to other verticals over time.