Lead Data Scientist, AdTech

Launch Potato•Charlotte, FL

14d•Remote

About The Position

Launch Potato is hiring a hands-on individual-contributor Lead Data Scientist to develop and operate models that determine which auto insurance carrier each shopper sees on a click wall, with the goal of improving conversions and carrier bids. The role also owns media-buying models that guide spend and are measured against return on ad spend (ROAS). Carrier conversion data—including quotes, policies, and acquisition cost—is becoming available for use in production models. The work involves delayed outcomes that may arrive weeks or months after a click, sparse sessions, repeat shoppers, and changing market conditions. Because bids influence which users are acquired, the models also affect the data available for future training. The data scientist frames problems with business partners, finds and works with the data, builds and validates models, and follows them through deployment and monitoring, alongside an ML engineer who supports production implementation. The initial focus is auto insurance, with scope expected to expand to other verticals over time. The company is remote-first, and most employees work East Coast hours. Success milestones include independently running insurance marketplace media buying by day 60. By the end of the first year, success means matching users to suitable carriers on the click wall, using carrier conversion data in production models, scaling automated marketplace bidding beyond proof of concept, establishing an approach for repeat users and sparse sessions, maintaining models through drift and changing objectives, and producing trusted outputs that the business can use without rework.

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 functions, architecture choices, and neural network fundamentals.
Understanding of how bids affect future training data, with experience handling late labels, sparse features, or drift.
Ability to own models end to end, from finding data through building, deployment, and monitoring.
Expert Python and SQL skills.

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 models that select the auto insurance carrier shown to each shopper on the click wall, aiming to improve conversion.
Own media-buying models that guide spend and are measured against ROAS.
Use carrier conversion data, including quotes, policies, and acquisition cost, in production models.
Run insurance marketplace media buying independently by day 60.
Take automated marketplace bidding from proof of concept to scale.
Develop an approach for repeat users and sparse sessions.
Frame problems with business partners; find data; build and validate models; and oversee deployment and monitoring.
Monitor and maintain production models as data and market conditions drift and objectives change.