Lead Data Scientist, AdTech

Launch Potato•Austin, TX

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 shoppers to the carrier most likely to convert them, using carrier conversion information such as quotes, policies, and acquisition cost. It will also own buying models for media spend, which are evaluated on return on ad spend (ROAS). 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 must account for the effect of decisions on future training data. The person in this role will frame business problems, locate and analyze data, build and validate models, and guide them through deployment and monitoring, working alongside an ML engineer on production implementation. Auto insurance is the initial focus, with scope expected to expand to other verticals over time. The position is remote-first, and most of the company works East Coast hours. Success includes independently running insurance marketplace media buying by day 60, putting carrier conversion data into production models, scaling automated bidding, addressing repeat users and sparse sessions, and delivering reliable models and outputs trusted by the business.

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 one's resume in detail, 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 budgets 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 with ECS, S3, Athena, or SageMaker.
CI/CD deployment experience.

Responsibilities

Build click-wall models that match each auto insurance shopper with the carrier most likely to convert them.
Own the buying models behind media spend and optimize decisions against ROAS.
Put carrier conversion data, including quotes, policies, and acquisition cost, to work in production models.
Run insurance marketplace media buying independently by day 60.
Take automated marketplace bidding from proof of concept to scale.
Develop approaches for repeat users and sparse sessions.
Build production models that remain effective through market drift and changing objectives.
Frame problems with business partners, find relevant data, build and validate models, and carry them through deployment and monitoring.
Work with an ML engineer on production-side implementation.
Expand the modeling scope beyond auto insurance to other verticals over time.