Lead Data Scientist, AdTech

Launch Potato•Las Vegas, NV

1d•$175,000 - $200,000•Remote

About The Position

Launch Potato is hiring a hands-on individual contributor to build and operate data science models for its auto insurance marketplace and media buying. The role will develop click-wall models that match each shopper to the carrier most likely to convert, helping carriers see stronger traffic performance and raise bids. It will also own media-buying models that guide spend and are evaluated against return on ad spend (ROAS). The work uses carrier conversion outcomes—including quotes, policies, and acquisition cost—that may arrive weeks or months after a click. The models must account for sparse sessions, repeat shoppers, changing market conditions, and feedback loops in which bids influence which users are acquired and therefore the data available for future training. Auto insurance is the initial focus, with the scope expected to expand to other verticals. The data scientist frames business problems, finds and prepares data, builds and validates models, and follows them through deployment and monitoring. An ML engineer partners on production implementation. The role is remote-first, and most of the company works East Coast hours.

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 work on their resume in depth, including loss-function and architecture choices and neural network fundamentals.
Understanding of how bids affect future training data, with experience handling late labels, sparse features, or drift.
End-to-end ownership of models, including finding data, building through deployment and monitoring, and bringing an independent point of view.
Expert 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 click-wall models that match auto insurance shoppers with the carriers most likely to convert.
Own media-buying models that guide spend, bidding, or ad serving and are evaluated on ROAS.
Put carrier conversion data—including quotes, policies, and acquisition cost—to work in production models.
Address delayed outcomes, sparse sessions, repeat users, market drift, and the effects of bidding decisions on future training data.
Frame problems with business partners, find and work with data, build and validate models, and own deployment and monitoring.
Run insurance marketplace media buying independently by day 60.
Develop a clear approach for repeat users and sparse sessions and scale automated marketplace bidding from proof of concept.
Build production models that remain effective as conditions and objectives change, and deliver output trusted by the business.