Launch Potato
[BR] Staff Applied Scientist, AdTech at Launch Potato
Role overview
An at-a-glance view of the facts included with this [BR] Staff Applied Scientist, AdTech at Launch Potato listing.
- Company
- Launch Potato
- Location
- Brazil
- Work arrangement
- Remote
- Employment type
- Full-time
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Role overview
This job description has been rewritten in fresh language using the facts in the listing. Source credit and original listing details are available below.
Launch Potato is a remote-first discovery and customer-acquisition company whose consumer brands include FinanceBuzz, All About Cookies, and OnlyInYourState. The company operates across 18 countries and is headquartered in South Florida. This is a full-time, remote Staff Applied Scientist role based in Brazil; most of the company works East Coast hours.
You will develop models for Launch Potato’s auto insurance marketplace and media buying. The marketplace models select which insurance carrier a shopper sees, with the aim of matching users to carriers likely to convert. You will also own buying models for media spend, which are evaluated on return on ad spend (ROAS). The work will use carrier outcomes such as quotes, policies, and acquisition cost, while addressing delayed results, sparse sessions, returning shoppers, changing market conditions, and feedback between bids and future training data.
This is a hands-on individual contributor position. You will work directly with business partners to frame problems, source data, build and validate models, and oversee deployment and monitoring, with an ML engineer supporting production work. Auto insurance is the initial focus, with scope expected to expand to other verticals. The stated early milestone is independently running insurance marketplace media buying by day 60. By the end of the first year, goals include carrier-matching click-wall models, production use of carrier conversion data, scaled automated marketplace bidding, an approach to repeat users and sparse sessions, models resilient to drift and changing objectives, and trusted output that the business can use without rework.
Role at a glance: Launch Potato is hiring a Brazil-based remote Staff Applied Scientist to build and operate auto insurance marketplace and media-buying models.
Responsibilities mentioned in the listing
- Develop and operate models that select the insurance carrier shown to each shopper on the click wall.
- Own media-buying models for spend and bidding, with performance measured by ROAS.
- Use carrier quote, policy, and acquisition-cost outcomes in production models.
- Address delayed labels, sparse sessions, repeat users, market drift, and feedback effects between bidding and training data.
- Partner with business stakeholders to frame problems, source data, build and validate models, and follow them through deployment and monitoring.
- Take primary responsibility for insurance marketplace media buying by day 60, then support the stated first-year goals for production modeling and scaled automated bidding.
Qualifications stated in the listing
- At least 5 years of hands-on applied data science experience with measurable business impact.
- Experience building models that informed paid-media spending, bidding, or ad serving; attribution or reporting experience alone is insufficient.
- Ability to explain work listed on your résumé in detail, including loss functions, architecture choices, and neural-network fundamentals.
- Understanding of how bids affect future training data, and experience handling delayed labels, sparse features, or drift.
- Experience owning models end to end, from finding data through deployment and monitoring, and forming an independent point of view.
- Expert-level Python and SQL.
- Preferred: experience spending real advertising budgets in live auctions or building large-scale ranking or recommendation systems.
- Preferred: ability to explain technical tradeoffs clearly to non-technical partners and design for auction feedback loops, selection bias, and exploration.
Full details from the source listing
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
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.