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The Role Build and deploy applied data science models for auto insurance carrier matching, media buying, bidding, and ad serving. Own the full modeling lifecycle, including data discovery, validation, deployment, monitoring, and adaptation to late labels, sparse sessions, feedback loops, and market drift. Partner with business stakeholders and an ML engineer to scale production models and deliver measurable acquisition and ROAS outcomes. Summary Generated by Built In

WHO ARE WE?

Launch Potato is the discovery and conversion company. We reach tens of millions of people every month through our brands, including FinanceBuzz, All About Cookies, and OnlyInYourState. We turn that attention into real outcomes for our partners, some of the world's leading companies.

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.

WHY JOIN US?

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.

YOUR ROLE

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.

WHAT SUCCESS LOOKS LIKE

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

MUST HAVE

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 NICE TO HAVES

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

TOTAL COMPENSATION

Base salary is set according to market rates for the nearest major metro and varies based on Launch Potato's Levels Framework. Your compensation package includes a base salary, profit-sharing bonus, and competitive benefits. Launch Potato is a performance-driven company, which means once you are hired, future increases will be based on company and personal performance, not annual cost of living adjustments. Want to accelerate your career? Apply now! Since day one, we've been committed to having a diverse, inclusive team and culture. We are proud to be an Equal Employment Opportunity company. We value diversity, equity, and inclusion. We do not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. Read Full Description Skills Required 5+ years of hands-on applied data science experience with measurable business impact Experience using models to drive paid media decisions, including spend, bidding, or ad serving Ability to explain personal modeling work, including loss functions, architecture choices, and neural network fundamentals Experience handling feedback loops, late labels, sparse features, or model drift End-to-end ownership of models from data discovery and development through deployment and monitoring Expert proficiency in Python and SQL Experience with live-auction advertising and real media budgets Experience with large-scale ranking or recommendation systems Ability to explain technical tradeoffs to non-technical partners Experience designing for auction feedback loops, selection bias, and exploration Hands-on neural network experience, including text, creative, or user-behavior embeddings Experience with AWS data science services, including ECS, S3, Athena, or SageMaker Experience with CI/CD deployment View all jobs at Launch Potato View Launch Potato Profile Report Job