Suggested rewrite: Led a cross-functional initiative that improved [business outcome] by [measurable result], demonstrating experience relevant to this role...
Lead Data Scientist, AdTech
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- Remote from
- USA
- Salary
- USD 175k–200k / yr
- Department
- Data Science & Analytics
- Employment
- Full Time
- Experience
- Senior
- Published
- Apply before
- 26 Nov 2026
- Listing views
- 31
- Application actions
- 1
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The role, at a glance.
Launch Potato is seeking a lead-level individual contributor to build and operate machine-learning models for auto-insurance ad serving, carrier matching, and media bidding. The role owns the full data-science lifecycle, from framing commercial problems and sourcing data through validation, deployment, and monitoring. Key modeling challenges include delayed conversion labels, sparse sessions, repeat users, market drift, auction feedback loops, and selection bias. The successful candidate will partner with an ML engineer on production delivery while communicating model trade-offs and results directly to business stakeholders. This remote US role is performance-oriented and initially focuses heavily on the insurance marketplace before expanding to other verticals.
Role DNA
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Pace & Pressure
5/5Autonomy Level
5/5Communication Load
4/5Salary analysis
Estimated compensation compared with the broader US market for similar roles.
Core skills
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Sample interview questions
I would define the decision target around expected downstream value, using available early signals while incorporating delayed quote and policy labels through a time-aware training design. I would use cohort-based validation, censor recent observations appropriately, and monitor calibration separately by label maturity. Where appropriate, I would combine short-horizon proxy targets with longer-horizon policy outcomes and continually retrain as mature labels arrive.
A bidding policy determines which impressions or users the business wins, so observed conversion data is not a random sample of all available opportunities. I would log propensities and auction context, evaluate counterfactual approaches where feasible, preserve controlled exploration, and avoid interpreting performance only from selectively won traffic. Model monitoring should also detect distribution changes caused by policy updates.
I would begin with offline ranking and calibration metrics such as NDCG, log loss, AUC, expected conversion value, and segment-level performance. I would then validate using time-based holdouts to reflect drift and delayed labels. Final evaluation would be through a controlled online experiment measuring carrier conversion, revenue or bid response, user outcomes, and guardrails such as latency and concentration risk.
I would monitor input availability, feature distributions, prediction distributions, calibration, latency, decision coverage, and eventual business outcomes. Because labels are delayed, I would include leading indicators and compare performance across acquisition channel, carrier, device, geography, and user-recency segments. Alerts should trigger investigation or rollback criteria, while scheduled retraining and champion-challenger testing support safe iteration.
I would connect the choice to the business decision and quantify the expected impact, uncertainty, and operational trade-off in plain language. For example, I might explain that a more exploratory bidding strategy can sacrifice a small amount of short-term efficiency to discover better long-term opportunities and avoid overfitting to current traffic. I would use simple visuals, concrete examples, and clear success metrics rather than focusing on model architecture details.
About this role.
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
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.
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