Job TitleYou'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 carrierCarrier conversion data running in production modelsAutomated marketplace bidding taken from proof of concept to scaleA clear approach for repeat users and sparse sessionsProduction models that hold up through drift and changing objectivesOutput the business trusts and uses without reworkMUST HAVE5+ years of hands-on applied data science with measurable business impactYour 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 includedYou see how today's bids shape tomorrow's training data, and you've handled late labels, sparse features or driftYou own models end to end: you find your own data, build through deployment and monitoring, and bring a point of viewExpert Python and SQLNICE TO HAVESReal ad budget spent in live auctionsLarge-scale ranking or recommendation systemsExplaining tradeoffs to non-technical partners in plain termsDesigning for auction feedback loops, selection bias and explorationHands-on neural network work, such as embeddings of text, creative or user behaviorAWS for data science: ECS, S3, Athena, SageMakerCI/CD deploymentBase 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. 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. Experience: 5 years required