Job description

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. BASE SALARY: $175,000 to $200,000 per year, paid semi-monthly 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.

Requirements

Must-haves

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?
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.
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

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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlpythonsqlawsci/cd

Platform: greenhouse
Effort: quick