[![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 Data 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 2026Published38Listing views1Application actions9 Oct 2026Apply before Opportunity details ## About this role. AI SummaryThis Staff Data Scientist role owns end-to-end applied machine-learning work for a marketing and insurance vertical, with ROAS as the central business metric. The position requires hands-on development of production-oriented models for acquisition, lead quality, monetization, ranking, recommendations, and LTV optimization. The successful candidate will partner directly with business stakeholders and ML engineers, remaining accountable from problem framing through model monitoring and performance analysis. Strong expertise in Python, SQL, AWS, performance marketing, and measurable commercial outcomes is essential. The role is remote across specified Canadian locations and emphasizes high ownership, direct communication, and rapid iteration. ## 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 senior, high-impact applied data science role requiring deep ML expertise plus the ability to translate marketing and insurance business problems into profitable deployed models. Ownership extends through live ROAS outcomes, requiring both technical rigor and strong cross-functional judgment. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job medianHighly competitive$185,000US market range$150k–$220k0$242k AI insightNo actual salary, pay range, or compensation amount is disclosed in the posting. The figures shown are estimated annual USD base-salary market values for a US-based Staff Data Scientist specializing in performance marketing, applied ML, and revenue optimization; actual Canadian compensation may differ. ## 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)[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)[Multi-Armed Bandits](https://jobicy.com/jobs?search_keywords=Multi-Armed%20Bandits.md)[Recommendation Systems](https://jobicy.com/jobs?search_keywords=Recommendation%20Systems.md)[LTV Modeling](https://jobicy.com/jobs?search_keywords=LTV%20Modeling.md)[AdTech](https://jobicy.com/jobs?search_keywords=AdTech.md) Sample interview questionsHow would you frame a modeling problem intended to improve ROAS for an insurance acquisition campaign?I would first define the optimization unit, decision point, attribution window, and ROAS formula with stakeholders. I would then establish a baseline, audit conversion and revenue-label quality, account for delays and selection bias, and evaluate the model through offline metrics plus a controlled online experiment measuring incremental ROAS and lead quality. Describe how you would use a multi-armed bandit for paid-media optimization. I would define each eligible targeting, creative, or bidding policy as an arm and choose a reward aligned to contribution profit or quality-adjusted revenue rather than clicks alone. I would begin with conservative exploration, include guardrails for spend and quality, handle delayed conversions where necessary, and compare the policy against the existing allocation strategy through holdout testing. What steps do you take to ensure an ML model is ready for production deployment? I validate data lineage, leakage risk, feature availability, calibration, segment performance, and expected business impact before handoff. I document assumptions and decision thresholds, partner with ML engineering on serving and monitoring requirements, and define alerts for data drift, prediction drift, model quality, and core business metrics such as ROAS. How would you model lead quality when downstream outcomes occur weeks after a lead is generated? I would create labels that reflect mature downstream value while explicitly accounting for censoring and conversion delay. Depending on the data, I would use survival methods, delayed-feedback adjustments, or proxy labels calibrated to eventual value, then continuously refresh the model as additional outcomes mature. Tell us about how you communicate a complex modeling recommendation to nontechnical marketing stakeholders. I focus on the business decision, expected financial impact, confidence level, trade-offs, and recommended next action rather than algorithmic detail. I use clear visuals and plain language, explain relevant assumptions and risks, and establish an experiment or rollout plan that lets stakeholders evaluate results against agreed success 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. 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/152875-staff-data-scientist-marketing.md) > Annual salary information is not provided for this position. 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