About this role.
This lead-level applied data science role owns end-to-end modeling for an AdTech insurance vertical, with ROAS as the primary business metric. The successful candidate will frame problems with stakeholders, develop and validate models, partner with ML engineering for production deployment, and monitor live performance. Core technical work includes multi-armed bandits or reinforcement learning, recommendation and ranking systems, funnel optimization, monetization, and LTV modeling in AWS. The role requires strong Python and SQL expertise plus substantial experience in performance marketing, lead generation, paid media, or user-acquisition modeling. It is a highly hands-on, outcome-oriented position in a fast-changing remote-first organization.
Role DNA
A quick view of the complexity, pace, ownership and collaboration implied by the job description.
Job Complexity
5/5Pace & Pressure
5/5Autonomy Level
5/5Communication Load
5/5Salary analysis
Estimated compensation compared with the broader US market for similar roles.
Core skills
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Sample interview questions
I would explain the business baseline, the optimization target, available data, modeling approach, experimental design, and measured incremental impact. I would also discuss how I monitored performance after launch and adjusted for attribution lag, seasonality, and traffic-quality changes.
I would begin by defining the conversion and value signals, including qualified lead, downstream policy outcome where available, revenue, and cost. I would then create features across user intent, source, creative, funnel behavior, and advertiser quality; validate offline performance; and deploy through a controlled online experiment optimized for incremental ROAS and lead quality.
I would use a bandit when alternatives can be evaluated continuously and the business benefits from allocating more traffic to stronger options during learning. I would retain A/B testing when unbiased fixed allocation, clearer causal estimation, or longer-term outcome measurement is more important than short-term exploitation.
I would establish monitoring for data quality, feature drift, prediction distributions, calibration, latency, and business outcomes such as ROAS and lead acceptance rate. I would define alert thresholds, maintain a rollback or baseline strategy, investigate deviations with stakeholders, and schedule retraining based on both drift and observed outcome decay.
I would lead with the business decision, expected impact, assumptions, risks, and success metric rather than model mechanics. I would use simple visuals and concrete scenarios, document what will change operationally, and create an agreed measurement plan so stakeholders understand how results will be evaluated.
WHO ARE WE?
Launch Potato is the discovery and conversion company. We reach tens of millions of people every month through our brands, including FinanceBuzz, All About Cookies, and OnlyInYourState. 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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