Description review

Lead Data Scientist, AdTech

Launch Potato · USA · back to the listing

HR standards

56/100

needs work

Title ↔ description

83/100

solid

Reads as

Data Scientist

100% confident

What this role officially is

data scientist — ESCO, the EU occupation classification

Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.

Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist

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What the listing never says

  • 25 bullet points. Long requirement lists deter qualified candidates, who read them as hard gates. Scope clarity
  • No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency

The listing, marked up

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 now1 !

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.

How this was produced

Highlights are found by rule, not by a model: each one is a phrase matched at a known position, and every note is a template we wrote. The two scores come from a typed-decision model (Jev) that reads the listing against the official role definition and real listings for the same role, and returns probabilities rather than prose — it never writes any of the words on this page, and never chooses what to highlight.

Deterministic penalty applied to the HR score: 9 points (from 65 before penalties). Reviewed 21 Sep 2026.