About this role.
This 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
5/5Pace & 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, 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.
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.
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.
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.
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 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.
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.
Annual salary information is not provided for this position. Explore salary ranges for similar roles in our Salary Directory ›
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