Lead Data Scientist, AdTech

Launch Potato•Jersey City, NJ

2d•$175,000 - $200,000•Remote

About The Position

Launch Potato is hiring a hands-on individual-contributor Lead Data Scientist to build and operate models that determine which auto insurance carrier shoppers see on the company’s click wall and to own the buying models behind media spend, which are evaluated on ROAS. The role will put carrier conversion data—including quotes, policies, and acquisition cost—to work in production. It addresses delayed outcomes, sparse sessions, repeat shoppers, market drift, and the feedback loop in which bids influence which users are won and what data becomes available for future training. The person in this role frames problems with business partners, finds and works with the data, builds and validates models, and follows them through deployment and monitoring. An ML engineer works alongside the role on production implementation. Auto insurance is the initial focus, with scope expected to broaden to other verticals over time. The role is remote-first; most of the company works East Coast hours. Success includes independently running insurance marketplace media buying by day 60 and, within the first year, delivering production click-wall matching, models using carrier conversion data, scaled automated bidding, approaches for repeat users and sparse sessions, and trusted models that withstand drift and changing objectives.

Requirements

At least 5 years of hands-on applied data science experience with measurable business impact.
Models must have driven paid media decisions such as spend, bidding, or ad serving; attribution and reporting alone do not qualify.
Must have personally built the work represented on their resume and be able to explain it in depth, including loss-function and architecture choices and neural network fundamentals.
Understanding of how current bids shape future training data, with experience handling late labels, sparse features, or drift.
Experience owning models end to end, including finding data, building models, deployment, and monitoring.
Expert Python and SQL skills.

Nice To Haves

Experience spending 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.
Own media-buying models and marketplace bidding decisions, with performance measured against ROAS.
Use carrier conversion data, including quotes, policies, and acquisition cost, in production models.
Frame business problems, identify and obtain relevant data, build and validate models, and oversee deployment and monitoring.
Develop approaches for delayed outcomes, sparse sessions, repeat users, market drift, and feedback effects from bidding decisions.
Run insurance marketplace media buying independently by day 60.
Take automated marketplace bidding from proof of concept to scale and expand modeling scope to other verticals over time.