[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) # Staff Applied 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[Brazil](https://jobicy.com/job-region/brazil.md)SalaryUndisclosedDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceSeniorPublished28 Oct 2026Apply before27 Nov 2026Listing views62Application actions4Application 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 staff-level applied scientist to build production ML models for auto-insurance carrier matching, media buying, and automated bidding. The role focuses on delayed conversion outcomes, sparse sessions, repeat users, market drift, and feedback loops created by bidding decisions. This is a hands-on individual-contributor position with end-to-end ownership from business framing and data discovery through validation, deployment, and monitoring. Strong experience in paid-media decisioning, Python, SQL, model architecture, and communicating trade-offs to business partners is essential. The initial scope is auto insurance, with potential expansion into other consumer 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 staff-level applied ML role solving causal and operationally difficult advertising problems, including delayed labels, selection bias, drift, and auction feedback loops. The scientist is expected to independently own high-impact models in production and make decisions trusted by the business. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$190,000US market range (per year)$160k–$230k0$253.00k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer compensation was disclosed. The yearly cadence and USD estimates are inferred for a staff-level applied scientist focused on production advertising ML in the US market; actual pay may vary by hiring location and total-compensation structure. ## 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](https://jobicy.com/jobs?search_keywords=AdTech.md)[Media buying optimization](https://jobicy.com/jobs?search_keywords=Media%20buying%20optimization.md)[Automated bidding](https://jobicy.com/jobs?search_keywords=Automated%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)[Model monitoring](https://jobicy.com/jobs?search_keywords=Model%20monitoring.md)[Concept drift detection](https://jobicy.com/jobs?search_keywords=Concept%20drift%20detection.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)[Experimentation](https://jobicy.com/jobs?search_keywords=Experimentation.md) Sample interview questionsHow would you model carrier conversion when policy outcomes arrive weeks or months after a click?I would define clear interim and final labels, maintain time-aware training snapshots, and use delayed-feedback methods so recent clicks are not incorrectly treated as negatives. I would evaluate models by cohort maturity and continuously recalibrate predictions as outcomes arrive. How do media bids create selection bias in training data? A bid determines which impressions or users we win, so observed outcomes reflect both user intent and the prior bidding policy. I would account for logged propensities where possible, preserve exploration traffic, and evaluate policy changes with counterfactual or carefully designed online experiments. Describe how you would validate a carrier-ranking model before deployment. I would use temporally separated validation sets, assess ranking and calibration metrics by carrier and user cohort, and compare expected business value such as conversion-adjusted revenue. Before broad rollout, I would run a controlled experiment with guardrails for conversion rate, acquisition cost, carrier quality, and operational stability. What approach would you use for repeat users and sparse sessions? I would combine session features with durable user-history features while controlling for leakage and recency. Depending on data volume, I would test hierarchical features, embeddings, and fallback models so predictions remain reliable when history is limited. How would you explain a model trade-off to non-technical stakeholders? I would connect the choice to a measurable business outcome, such as incremental policies, ROAS, or acquisition cost, rather than focusing on algorithm terminology. I would present the expected upside, uncertainty, key risks, and the experiment or monitoring plan that allows us to make a reversible decision. 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. 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 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. Show more [Apply now >](https://jobicy.com/jobs/157568-staff-applied-scientist-adtech-2.md) > Annual salary information is not provided for this position. 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