[![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) # Lead Applied Scientist, Marketing 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 2026Published45Listing views5Application actions9 Oct 2026Apply before Opportunity details ## About this role. AI SummaryThis is a senior hands-on applied data science role focused on improving ROAS, media efficiency, lead quality, and revenue within the insurance vertical. The Lead Applied Scientist owns the workflow from business framing and model development through validation, deployment partnership, monitoring, and performance analysis. Core work includes adtech and paid-media modeling, recommendation and ranking systems, LTV prediction, funnel optimization, and multi-armed bandit or reinforcement-learning techniques. The role requires 5+ years of applied data science experience, advanced Python and SQL, AWS-based ML deployment experience, and direct collaboration with business stakeholders and ML engineers. It operates in a remote, high-change environment with strong ownership expectations and measurable commercial outcomes. ## 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 insightThe position combines advanced applied ML with direct accountability for live ROAS and lead-quality outcomes, requiring both rigorous modeling and commercial judgment. Success depends on independently identifying opportunities, navigating ambiguous business problems, and sustaining model performance in production. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job medianHighly competitive$195,000US market range$165k–$225k0$248k AI insightNo actual salary, pay range, or other role compensation was disclosed in the posting. These are estimated annual US-market base-salary figures in USD for a lead-level applied scientist specializing in marketing, adtech, and production machine learning; actual compensation may vary materially by hiring location, bonus, and equity. ## Core skills Skills and capabilities most closely associated with this opportunity. [Applied Machine Learning](https://jobicy.com/jobs?search_keywords=Applied%20Machine%20Learning.md)[Performance Marketing](https://jobicy.com/jobs?search_keywords=Performance%20Marketing.md)[AdTech](https://jobicy.com/jobs?search_keywords=AdTech.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)[Recommendation Systems](https://jobicy.com/jobs?search_keywords=Recommendation%20Systems.md)[Reinforcement Learning](https://jobicy.com/jobs?search_keywords=Reinforcement%20Learning.md)[LTV Modeling](https://jobicy.com/jobs?search_keywords=LTV%20Modeling.md) Sample interview questionsDescribe a model you built that improved paid-media efficiency or return on ad spend.I would explain the business baseline, optimization target, data sources, feature design, model choice, validation method, and production rollout. I would quantify the lift in ROAS or cost efficiency, explain guardrails for lead quality, and describe how I monitored performance after deployment. How would you approach building a buying model for an insurance acquisition vertical? I would begin by defining the conversion event, attribution window, profitability constraints, and quality signals with business stakeholders. I would combine user, creative, placement, funnel, and historical conversion features; train calibrated value or propensity models; validate results through time-based tests; and optimize toward expected profit or ROAS rather than volume alone. When would you use a multi-armed bandit instead of a conventional supervised-learning model? I would use a multi-armed bandit when the system must continuously balance exploration of uncertain options with exploitation of known high-performing options, such as allocating spend across creatives, placements, or content variants. A supervised model is more suitable when robust labeled historical data exists and the decision environment is relatively stable. How do you ensure that an offline model improvement translates into a reliable production result? I use leakage-safe, time-based evaluation and align offline metrics with the live business metric. Before broad rollout, I define monitoring for calibration, data drift, quality, spend, and ROAS; launch with controlled experiments or phased traffic; and retain rollback criteria if performance does not meet expectations. How do you communicate modeling trade-offs to nontechnical stakeholders? I anchor the discussion in the decision, expected business impact, assumptions, and risks rather than algorithmic detail. I use clear visualizations and scenarios, state confidence levels and limitations, and agree on success metrics, test design, and next actions so stakeholders can make informed decisions. ### 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. 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 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/152852-lead-applied-scientist-marketing.md) > Annual salary information is not provided for this position. 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