[![Image]() Meet Jobicy Copilot — free AI autofill for job applications + remote job alerts ›](#) [All remote jobs](https://jobicy.com/jobs.md)Open role[![Launch Potato logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/06/30f8890f-221-1.png)](https://jobicy.com/company/launch-potato.md)Remote opportunity at[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)Share9 Sep 2026Published54Listing views2Application actions9 Oct 2026Apply before Opportunity details ## About this role. AI SummaryLaunch Potato is hiring a Staff Applied Scientist to own end-to-end applied machine-learning work for its Insurance and Advertiser Quality verticals. The role focuses on improving ROAS, lead quality, media efficiency, and revenue through deployed adtech, recommendation, ranking, LTV, and optimization models. The scientist will frame business problems with stakeholders, build and validate models in AWS, partner with ML Engineering on production deployment, and monitor live performance. This is a highly hands-on senior individual-contributor role requiring deep Python and SQL expertise plus experience in paid media, lead generation, or performance marketing. ## 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 5/5IndependentCollaborative AI insightThis is a staff-level applied science position with ownership from ambiguous commercial problems through production model performance and measurable ROAS outcomes. It requires advanced ML breadth, strong adtech domain judgment, and the ability to influence business and engineering partners in a fast-changing environment. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job medianMarket rate$187,500US market range$165k–$230k0$253k AI insightThe disclosed base-salary range is $175,000–$200,000 USD yearly, with a midpoint of $187,500. For a US-based staff-level applied scientist specializing in adtech and performance marketing, an estimated base-salary market range is approximately $165,000–$230,000 yearly; bonus and profit-sharing may increase total compensation but are not quantified in the posting. ## Core skills Skills and capabilities most closely associated with this opportunity. [Applied Machine Learning](https://jobicy.com/jobs?search_keywords=Applied%20Machine%20Learning.md)[AdTech](https://jobicy.com/jobs?search_keywords=AdTech.md)[Performance Marketing](https://jobicy.com/jobs?search_keywords=Performance%20Marketing.md)[ROAS Optimization](https://jobicy.com/jobs?search_keywords=ROAS%20Optimization.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[AWS](https://jobicy.com/jobs?search_keywords=AWS.md)[Reinforcement Learning](https://jobicy.com/jobs?search_keywords=Reinforcement%20Learning.md)[Recommendation Systems](https://jobicy.com/jobs?search_keywords=Recommendation%20Systems.md)[LTV Modeling](https://jobicy.com/jobs?search_keywords=LTV%20Modeling.md) Sample interview questionsDescribe a machine-learning model you built that directly improved a business metric such as ROAS, conversion rate, or lead quality.I would explain the initial business problem, the baseline metric, data sources, model choice, experimentation design, production rollout, and measured lift. I would also describe how I monitored performance and adjusted for attribution delays, changing traffic mix, and model drift. How would you approach building a buying model for an insurance acquisition vertical with a positive-ROAS constraint? I would begin by defining the conversion and revenue attribution windows, unit economics, quality signals, and allowable risk threshold. I would build a calibrated value or conversion-propensity model, incorporate expected revenue and cost, validate it through offline back-testing and controlled experiments, then optimize bids or routing decisions subject to ROAS guardrails. When would you use a multi-armed bandit instead of a traditional A/B test in paid media optimization? I would use a multi-armed bandit when traffic allocation needs to adapt continuously and the cost of sending substantial traffic to weak variants is high. A/B testing remains useful when clean causal comparison, stable allocation, and statistical certainty are more important than short-term optimization. How do you partner effectively with ML Engineering when handing off a validated model for production deployment? I provide clear feature definitions, reproducible training and scoring code, model artifacts, expected input-output contracts, evaluation results, and monitoring requirements. I stay engaged through integration, launch validation, and post-deployment analysis rather than treating deployment as a one-time handoff. How would you evaluate whether a lead-quality model is creating durable value rather than optimizing a short-term proxy? I would connect model performance to downstream outcomes such as qualified leads, approval or sale rates, revenue, refunds, and retention where available. I would use delayed-label evaluation, cohort analysis, calibration checks, and holdout experiments to ensure gains persist beyond immediate click or form-submit metrics. ### 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 MUST HAVE: * Proven experience in digital marketing, performance marketing, or the leadgen industry * Building adtech algorithms and supporting user acquisition or paid media modeling (highly desired) * Strong modeling fundamentals: the ability to build effective models that drive business impact * Multi-year, hands-on experience building and deploying ML solutions in the AWS cloud * Hands-on experience across core technique areas: multi-armed bandit / reinforcement learning, recommendation and ranking systems (content-based, collaborative filtering, hybrid), funnel and monetization optimization, LTV modeling * Expert Python and SQL EXPERIENCE: 5+ years in a hands-on, in-the-weeds applied data science role delivering measurable business impact. YOUR ROLE Own the full data science engine for a priority vertical, from business problem to deployed model to live ROAS performance, driving measurable revenue and media efficiency. This is a hands-on, in-the-weeds role: you are heavily immersed in the data and the modeling, framing the business problem directly with stakeholders, building and validating the model, handing the ML-engineering last mile to your ML engineering partner, and staying engaged through deployment, monitoring, and performance analysis. You will start focusing on Insurance and Advertiser Quality, with scope that broadens over time. Your primary metric is ROAS. OUTCOMES * Own the Insurance vertical’s primary modeling work end-to-end with measurable ROAS impact * Deliver buying models that maintain positive ROAS and quality * Drive lead quality improvements across our portfolio of brands: Messaging, Funnels, Content/Listicles, and more resulting in measurable impact to revenue growth * Establish trusted, direct partnership with vertical business stakeholders * Produce trusted output: validated, documented, low correction burden * Identify and leverage net-new modeling opportunities the business has not flagged COMPETENCIES * Business-first framing: Starts with the problem and the metric, not the model. * Full-stack ownership: Stays engaged from problem definition through deployed performance * Proactive communication: Closes loops without being chased * Collaborative: Leans on ML engineering for the last mile rather than working solo * Coachable: Seeks feedback and turns it into visible behavior change * Curiosity paired with delivery discipline NICE TO HAVES * Sophisticated ML at companies where paid digital media is core to the business model * Creative embeddings work: incorporating embeddings of creatives, videos, headlines, and search into paid media models * Insurance domain experience * Creating state-of-the-art Ad Ranking algorithms * Modeling against ad-platform data points (Google, Meta, native) * LLMs / deep learning applied to personalization or content * Familiarity with Looker 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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