Lead Data Scientist, AdTech

Launch Potato•Chicago, KS

18h•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 models that match shoppers with the carrier most likely to convert them, using carrier conversion data such as quotes, policies, and acquisition costs. It will also own media-buying models that guide spend and 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. Bidding decisions also affect which users are acquired, shaping future training data. The role owns problems end to end: framing them with business partners, finding and preparing data, building and validating models, and following them through deployment and monitoring. An ML engineer supports production work. Auto insurance is the initial focus, with scope expected to broaden to other verticals over time. The company is remote-first, and most employees work East Coast hours. Success includes independently running insurance marketplace media buying by day 60; launching click-wall serving that matches users to carriers; putting carrier conversion data into production models; scaling automated marketplace bidding; developing approaches for repeat users and sparse sessions; and delivering reliable production models and trusted business outputs.

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 a resume in detail, including loss functions and architecture choices, and demonstrate neural network fundamentals.
Understanding of how bidding shapes future training data, with experience handling late labels, sparse features, or drift.
End-to-end model ownership, 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 work, 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 determine which auto insurance carrier each shopper sees on the click wall.
Own media-buying models that guide spend and are evaluated on ROAS.
Use carrier conversion data, including quotes, policies, and acquisition costs, in production models.
Frame problems directly with business partners and independently find the required data.
Build and validate models, then see them through deployment and monitoring.
Run insurance marketplace media buying independently by day 60.
Develop approaches for repeat users, sparse sessions, delayed outcomes, and changing market conditions.
Take automated marketplace bidding from proof of concept to scale.
Build production models that remain effective as data and business objectives change.
Deliver model outputs that business partners trust and use without rework.