Lead Data Scientist, AdTech

Launch Potato
United States
Remote

Who this role is best for

Best suited to data scientists with end-to-end model ownership experience working in adtech and insurance domains, preferably with remote-first flexibility and a track record of deploying models in production.

Best fit for

  • Individuals who have worked with carrier conversion data and built models for paid media decisions, such as bidding or ad serving.
    — “Your models drove paid media decisions: spend, bidding or ad serving.”
  • Candidates with strong Python and SQL skills who can navigate complex data pipelines and understand auction dynamics.
    — “Expert Python and SQL”

Things to consider

  • The role is heavily focused on production deployment and model monitoring, not just research or experimentation.
    — “You own models end to end: you find your own data, build through deployment and monitoring, and bring a point of view”
  • The role is remote-first but assumes alignment with East Coast working hours, which may affect flexibility for candidates in other time zones.
    — “We're remote first, and most of the company works East Coast hours.”

How to stand out

  • Emphasize your experience with modeling conversion data and handling drift or sparse features.
    — “You see how today's bids shape tomorrow's training data, and you've handled late labels, sparse features or drift”
  • Demonstrate your ability to work with carrier conversion metrics like quotes, policies, and acquisition cost.
    — “Carrier conversion data (quotes, policies and acquisition cost) is now coming back to us, and you'll be the one who puts it to work in production.”
  • Showcase your experience with ranking or recommendation systems, particularly in adtech or media buying.
    — “Large-scale ranking or recommendation systems”
  • Demonstrate your ability to explain complex model tradeoffs and technical decisions to non-technical stakeholders.
    — “Explaining tradeoffs to non-technical partners in plain terms”
Pace · Fast PacedCollaboration · MediumAutonomy · MediumDecision Impact · Company

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • builds models that decide which carrier each auto insurance shopper sees
  • carrier conversion data running in production models
Typical background
5+ years of hands-on applied data science

Skills & requirements

Required

5+ Years Of Hands-on Applied Data ScienceExpert Python And SQLOwns Models End To EndHandles Late Labels, Sparse Features Or Drift

Preferred

Real Ad Budget Spent In Live AuctionsLarge-scale Ranking Or Recommendation SystemsExplaining Tradeoffs To Non-technical Partners

Stack & domain

PythonSQLData ScienceMachine LearningNeural NetworksProblem FramingData ValidationModel DeploymentMonitoringCommunicationAdvertisingData Analysis

About the role

Original posting from Launch Potato

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.

YOUR ROLE

You'll build the models that decide which carrier each auto insurance shopper sees on our click wall. The goal is to send every user to the carrier most likely to convert them, so carriers see our traffic convert and raise their bids. You'll also own the buying models behind our media spend, which are held to ROAS.

Carrier conversion data (quotes, policies and acquisition cost) is now coming back to us, and you'll be the one who puts it to work in production. The hard part is the data. Outcomes land weeks or months after the click, many sessions are sparse, the same shoppers come back, and the market drifts. Our bids also decide which users we win, so the model shapes the data it learns from next.

This is a hands-on individual contributor role. You frame problems directly with the business, find your own data, build and validate the model, and stay with it through deployment and monitoring. An ML engineer works alongside you on the production side. Auto insurance takes most of your time to start, and scope broadens to other verticals over time.

We're remote first, and most of the company works East Coast hours.

WHAT SUCCESS LOOKS LIKE

By day 60, you run our insurance marketplace media buying on your own.

By the end of your first year:

Click wall serving that matches each user to the right carrier

Carrier conversion data running in production models

Automated marketplace bidding taken from proof of concept to scale

A clear approach for repeat users and sparse sessions

Production models that hold up through drift and changing objectives

Output the business trusts and uses without rework

MUST HAVE

5+ years of hands-on applied data science with measurable business impact

Your models drove paid media decisions: spend, bidding or ad serving. Attribution and reporting alone don't qualify.

You built what's on your resume and can explain it down to the loss function and architecture choice, neural network fundamentals included

You see how today's bids shape tomorrow's training data, and you've handled late labels, sparse features or drift

You own models end to end: you find your own data, build through deployment and monitoring, and bring a point of view

Expert Python and SQL

NICE TO HAVES

Real ad budget spent in live auctions

Large-scale ranking or recommendation systems

Explaining tradeoffs to non-technical partners in plain terms

Designing for auction feedback loops, selection bias and exploration

Hands-on neural network work, such as embeddings of text, creative or user behavior

AWS for data science: ECS, S3, Athena, SageMaker

CI/CD deployment

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.

Source: Launch Potato careers

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