[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 Data Scientist, Lead Generation 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 views54Application 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 individual contributor to build production machine-learning models for auto-insurance carrier matching and media-buying optimization. The role owns the full data-science lifecycle, from framing commercial problems and sourcing data through model validation, deployment partnership, and ongoing monitoring. Key challenges include delayed conversion labels, sparse sessions, repeat users, market drift, and auction-driven feedback loops that affect future training data. The successful candidate will apply advanced Python, SQL, ranking or recommendation methods, and experimentation to improve conversion performance and ROAS. This is a high-impact remote role restricted to Brazil, with substantial autonomy and collaboration with business stakeholders and an ML engineer. ## 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 senior applied ML role with difficult causal and operational challenges, including selection bias, delayed outcomes, drift, and feedback loops in live bidding systems. The candidate must independently turn ambiguous business objectives into reliable production models with measurable financial impact. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$175,000US market range (per year)$150k–$220k0$242.00k 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 displayed figures are AI estimates in USD for a staff-level applied data scientist specializing in production ML, ranking, ad auctions, and growth optimization; the annual cadence is inferred from the senior full-time role and market conventions. ## 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)[Predictive modeling](https://jobicy.com/jobs?search_keywords=Predictive%20modeling.md)[Ranking systems](https://jobicy.com/jobs?search_keywords=Ranking%20systems.md)[Recommendation systems](https://jobicy.com/jobs?search_keywords=Recommendation%20systems.md)[Media buying optimization](https://jobicy.com/jobs?search_keywords=Media%20buying%20optimization.md)[Real-time bidding](https://jobicy.com/jobs?search_keywords=Real-time%20bidding.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)[Concept drift detection](https://jobicy.com/jobs?search_keywords=Concept%20drift%20detection.md)[Causal inference](https://jobicy.com/jobs?search_keywords=Causal%20inference.md)[AWS SageMaker](https://jobicy.com/jobs?search_keywords=AWS%20SageMaker.md)[CI/CD](https://jobicy.com/jobs?search_keywords=CICD.md) Sample interview questionsHow would you build a model to select the best insurance carrier for each shopper when conversion labels arrive weeks after the click?I would define the decision point and available real-time features, then train a calibrated conversion or expected-value model using delayed-label handling and strict time-based validation. I would use survival-style or maturation adjustments where appropriate, retain prediction snapshots for later attribution, and continuously monitor performance as labels mature. How do auction feedback loops and selection bias affect a media-buying model? A bidding policy determines which impressions or users we win, so observed outcomes are not a random sample. I would log propensities and auction context, evaluate counterfactually where possible, use exploration to maintain coverage, and separate model changes from shifts caused by the serving policy. What metrics would you use to evaluate a carrier-ranking model? I would combine offline ranking and calibration metrics, such as NDCG, log loss, and calibration error, with business metrics including conversion rate, expected revenue per click, acquisition cost, carrier-level quality, and incremental ROAS. Online experiments would be the final decision criterion. Describe how you would detect and respond to model drift in this marketplace. I would monitor feature distributions, score distributions, label delay and maturity, calibration, conversion outcomes, and business KPIs by carrier and traffic segment. When drift is detected, I would diagnose whether it stems from traffic, carrier behavior, auction policy, or data quality, then retrain, recalibrate, or adjust features and guardrails as needed. How would you explain a tradeoff between short-term ROAS and exploration to a non-technical stakeholder? I would explain that always choosing the current best option can prevent us from learning when conditions change. A controlled exploration budget may slightly reduce immediate efficiency, but it provides the evidence needed to uncover stronger opportunities and protect future ROAS. 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/157573-staff-data-scientist-lead-generation.md) > Annual salary information is not provided for this position. 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