[All remote jobs](https://jobicy.com/jobs.md)[![Launch Potato logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/06/30f8890f-221-1.png)](https://jobicy.com/company/launch-potato.md)[Launch Potato](https://jobicy.com/company/launch-potato.md) # Lead Data Scientist, AdTech Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/launch-potato.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md)SalaryUSD 175k–200k / yrDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceSeniorPublished27 Oct 2026Apply before26 Nov 2026Listing views43Application actions2Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Launch Potato is seeking a lead-level individual contributor to build and operate machine-learning models for auto-insurance ad serving, carrier matching, and media bidding. The role owns the full data-science lifecycle, from framing commercial problems and sourcing data through validation, deployment, and monitoring. Key modeling challenges include delayed conversion labels, sparse sessions, repeat users, market drift, auction feedback loops, and selection bias. The successful candidate will partner with an ML engineer on production delivery while communicating model trade-offs and results directly to business stakeholders. This remote US role is performance-oriented and initially focuses heavily on the insurance marketplace before expanding to other verticals. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightThis is a technically demanding applied ML role involving production decisioning systems with delayed labels, feedback loops, drift, and direct ROAS accountability. It requires deep hands-on modeling expertise as well as independent commercial judgment and end-to-end ownership. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$187,500US market range (per year)$175k–$220k0$242.00k AI insightThe employer explicitly lists a base salary of $175,000–$200,000 USD per year, paid semi-monthly. The midpoint is $187,500 annually. Estimated US market compensation for a lead applied data scientist specializing in advertising optimization and production ML is approximately $175,000–$220,000 per year, excluding bonus and profit sharing. ## Core skills Skills and capabilities most closely associated with this opportunity. [Applied machine learning](https://jobicy.com/jobs?search_keywords=Applied%20machine%20learning.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[AdTech optimization](https://jobicy.com/jobs?search_keywords=AdTech%20optimization.md)[Media bidding](https://jobicy.com/jobs?search_keywords=Media%20bidding.md)[Conversion modeling](https://jobicy.com/jobs?search_keywords=Conversion%20modeling.md)[Ranking systems](https://jobicy.com/jobs?search_keywords=Ranking%20systems.md)[Recommendation systems](https://jobicy.com/jobs?search_keywords=Recommendation%20systems.md)[Auction modeling](https://jobicy.com/jobs?search_keywords=Auction%20modeling.md)[ROAS optimization](https://jobicy.com/jobs?search_keywords=ROAS%20optimization.md)[Model deployment](https://jobicy.com/jobs?search_keywords=Model%20deployment.md)[Model monitoring](https://jobicy.com/jobs?search_keywords=Model%20monitoring.md)[AWS SageMaker](https://jobicy.com/jobs?search_keywords=AWS%20SageMaker.md)[Amazon Athena](https://jobicy.com/jobs?search_keywords=Amazon%20Athena.md)[CI/CD](https://jobicy.com/jobs?search_keywords=CICD.md) Sample interview questionsHow would you design a model to choose the best insurance carrier for each shopper when policy outcomes arrive weeks after the click?I would define the decision target around expected downstream value, using available early signals while incorporating delayed quote and policy labels through a time-aware training design. I would use cohort-based validation, censor recent observations appropriately, and monitor calibration separately by label maturity. Where appropriate, I would combine short-horizon proxy targets with longer-horizon policy outcomes and continually retrain as mature labels arrive. How do auction feedback loops and selection bias affect a media-buying model? A bidding policy determines which impressions or users the business wins, so observed conversion data is not a random sample of all available opportunities. I would log propensities and auction context, evaluate counterfactual approaches where feasible, preserve controlled exploration, and avoid interpreting performance only from selectively won traffic. Model monitoring should also detect distribution changes caused by policy updates. Describe how you would evaluate a ranking model used on a carrier click wall. I would begin with offline ranking and calibration metrics such as NDCG, log loss, AUC, expected conversion value, and segment-level performance. I would then validate using time-based holdouts to reflect drift and delayed labels. Final evaluation would be through a controlled online experiment measuring carrier conversion, revenue or bid response, user outcomes, and guardrails such as latency and concentration risk. What is your approach to production monitoring for a model with sparse features and changing market conditions? I would monitor input availability, feature distributions, prediction distributions, calibration, latency, decision coverage, and eventual business outcomes. Because labels are delayed, I would include leading indicators and compare performance across acquisition channel, carrier, device, geography, and user-recency segments. Alerts should trigger investigation or rollback criteria, while scheduled retraining and champion-challenger testing support safe iteration. How would you explain a complex model trade-off to a non-technical stakeholder? I would connect the choice to the business decision and quantify the expected impact, uncertainty, and operational trade-off in plain language. For example, I might explain that a more exploratory bidding strategy can sacrifice a small amount of short-term efficiency to discover better long-term opportunities and avoid overfitting to current traffic. I would use simple visuals, concrete examples, and clear success metrics rather than focusing on model architecture details. Opportunity details ## About this role. ### WHO ARE WE? [Launch Potato](https://launchpotato.com/careers?utm_source=greenhouse&utm_medium=jobpost&utm_campaign=careers-back-to-careers) is the discovery and conversion company. We reach tens of millions of people every month through our brands, including [FinanceBuzz](https://financebuzz.com/?utm_source=greenhouse&utm_medium=jobpost&utm_campaign=careers-to-brand), [All About Cookies](https://allaboutcookies.org/?utm_source=greenhouse&utm_medium=jobpost&utm_campaign=careers-to-brand), and [OnlyInYourState](https://www.onlyinyourstate.com/?utm_source=greenhouse&utm_medium=jobpost&utm_campaign=careers-to-brand). 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. 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