Lead Data Scientist, AdTech

Launch Potato•Atlanta, GA

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 advertising technology and insurance marketplace. The role will develop models that determine which auto insurance carrier each shopper sees on a click wall, with the goal of directing users to carriers most likely to convert. It will also own media-buying models that guide spend and are evaluated on return on ad spend (ROAS). The work includes putting carrier conversion data—such as quotes, policies, and acquisition costs—into production models. Key challenges include delayed outcomes, sparse sessions, returning users, changing market conditions, and feedback loops in which bidding decisions affect the future training data. The person in this role will frame problems with business partners, identify and prepare data, build and validate models, and carry them through deployment and monitoring, working alongside an ML engineer on production implementation. Auto insurance is the initial focus, with scope expected to broaden to other verticals over time. The company is remote-first, and most of the team 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 involving 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 demonstrate neural network fundamentals.
Understanding of how current bids shape future training data, with experience handling late labels, sparse features, or drift.
Ability to own models end to end, from finding data through deployment and monitoring, and to bring an informed point of view.
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 for text, creative, or user behavior.
AWS data science experience with ECS, S3, Athena, or SageMaker.
CI/CD deployment experience.

Responsibilities

Build models that match auto insurance shoppers with the carriers most likely to convert.
Own media-buying models that guide spend and are measured on ROAS.
Use carrier conversion data, including quotes, policies, and acquisition costs, in production models.
Address delayed labels, sparse sessions, repeat users, market drift, and the effects of bidding decisions on future training data.
Frame problems directly with business partners and identify the data needed to solve them.
Build, validate, deploy, and monitor models end to end.
Work with an ML engineer on production implementation.
Initially focus on auto insurance, with the role's scope broadening to other verticals over time.
Run insurance marketplace media buying independently by day 60.