Lead Data Scientist, AdTech

Launch Potato•St. Louis, MO

1d•$175,000 - $200,000•Remote

About The Position

Launch Potato is seeking a hands-on individual contributor to build and operate data science models for its auto insurance marketplace and media buying. The role will develop models that match shoppers with the carrier most likely to convert them on the click wall, helping carriers achieve conversions and raise bids. It will also own buying models for media spend, which are evaluated on return on ad spend (ROAS). The work will make use of carrier conversion data, including quotes, policies, and acquisition cost. Challenges include delayed outcomes, 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. The role frames problems with business partners, finds and prepares data, builds and validates models, and follows them through deployment and monitoring, working alongside an ML engineer on production implementation. Auto insurance will be the initial focus, with scope expected to expand to other verticals. Launch Potato is a remote-first company, and most employees work East Coast hours. Success includes independently running insurance marketplace media buying by day 60; putting carrier conversion data into production models; scaling automated marketplace bidding; establishing approaches for repeat users and sparse sessions; maintaining models through drift and changing objectives; and producing trusted output used by the business 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, such as spend, bidding, or ad serving; attribution and reporting alone do not qualify.
Ability to explain models personally built, including loss-function and architecture choices and neural network fundamentals.
Understanding of how bids influence future training data, and experience handling late labels, sparse features, or drift.
End-to-end ownership of models, from finding data and building through deployment and monitoring.
Expert Python and SQL skills.

Nice To Haves

Experience spending a real ad budget in live auctions.
Experience with large-scale ranking or recommendation systems.
Ability to explain tradeoffs to non-technical partners in plain terms.
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, including ECS, S3, Athena, or SageMaker.
CI/CD deployment experience.

Responsibilities

Build models that match auto insurance shoppers with the carrier most likely to convert them on the click wall.
Own media-buying models and optimize them against ROAS.
Put carrier conversion data, including quotes, policies, and acquisition cost, to work in production models.
Frame problems with business stakeholders, identify and obtain data, build and validate models, and oversee deployment and monitoring.
Run insurance marketplace media buying independently by day 60.
Develop an approach for repeat users and sparse sessions.
Take automated marketplace bidding from proof of concept to scale.
Ensure production models remain effective amid drift and changing objectives.
Work with an ML engineer on production implementation.
Expand the role's modeling scope to additional verticals over time.