Lead Data Scientist, AdTech

Our mission: create predictable, high-value customer acquisition at scale by connecting discovery, intent, and performance across category-leading consumer brands in finance, travel, and entertainment.

We run profitable and bootstrapped, with a South Florida HQ and a remote-first team across 18 countries.

You own outcomes here, not tasks. We measure work by impact, not activity. The pace is real, and change is constant, so the people who do best turn change into opportunity instead of waiting for it to settle. Feedback runs direct and lands with respect. Ordinary people make themselves extraordinary at Launch Potato.

You'll build the models that decide which carrier each auto insurance shopper sees on our click wall. The goal is to send every user to the carrier most likely to convert them, so carriers see our traffic convert and raise their bids. You'll also own the buying models behind our media spend, which are held to ROAS.
Carrier conversion data (quotes, policies and acquisition cost) is now coming back to us, and you'll be the one who puts it to work in production. The hard part is the data. Outcomes land weeks or months after the click, many sessions are sparse, the same shoppers come back, and the market drifts. Our bids also decide which users we win, so the model shapes the data it learns from next.
This is a hands-on individual contributor role. You frame problems directly with the business, find your own data, build and validate the model, and stay with it through deployment and monitoring. An ML engineer works alongside you on the production side. Auto insurance takes most of your time to start, and scope broadens to other verticals over time.
We're remote first, and most of the company works East Coast hours.
By day 60, you run our insurance marketplace media buying on your own.
By the end of your first year:
Click wall serving that matches each user to the right carrier
Carrier conversion data running in production models
Automated marketplace bidding taken from proof of concept to scale
A clear approach for repeat users and sparse sessions
Production models that hold up through drift and changing objectives
Output the business trusts and uses without rework

5+ years of hands-on applied data science with measurable business impact
Your models drove paid media decisions: spend, bidding or ad serving. Attribution and reporting alone don't qualify.
You built what's on your resume and can explain it down to the loss function and architecture choice, neural network fundamentals included
You see how today's bids shape tomorrow's training data, and you've handled late labels, sparse features or drift
You own models end to end: you find your own data, build through deployment and monitoring, and bring a point of view
Expert Python and SQL
Real ad budget spent in live auctions
Large-scale ranking or recommendation systems
Explaining tradeoffs to non-technical partners in plain terms
Designing for auction feedback loops, selection bias and exploration
Hands-on neural network work, such as embeddings of text, creative or user behavior
AWS for data science: ECS, S3, Athena, SageMaker
CI/CD deployment