Launch Potato
[BR] Staff Data Scientist, Lead Generation at Launch Potato
Role overview
An at-a-glance view of the facts included with this [BR] Staff Data Scientist, Lead Generation 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 profitable, bootstrapped discovery and customer-acquisition company whose brands include FinanceBuzz, All About Cookies, and OnlyInYourState. It reaches tens of millions of people monthly and operates remotely across 18 countries, with a South Florida headquarters. This is a full-time, remote Staff Data Scientist role based in Brazil; most of the company works East Coast hours.
You will develop and operate models that choose which auto insurance carrier a shopper sees on a click wall and guide media buying, with buying models measured against return on ad spend (ROAS). Carrier data on quotes, policies, and acquisition costs is becoming available for production use. Modeling challenges include delayed outcomes, sparse sessions, repeat shoppers, market changes, and feedback effects in which bids influence both acquired users and future training data.
This is a hands-on individual-contributor position. You will work directly with business partners to define problems, locate data, build and validate models, and carry them through deployment and monitoring, alongside an ML engineer on production implementation. Auto insurance is the initial focus, with the role expected to expand into other verticals. The first 60-day goal is to manage insurance marketplace media buying independently. By the end of year one, expected outcomes include carrier-matching click-wall models, production use of carrier conversion data, scaled automated bidding, approaches for repeat users and sparse sessions, models resilient to drift and changing objectives, and output the business can use without rework.
Role at a glance: Remote, full-time Staff Data Scientist role in Brazil building production models for auto insurance carrier matching and ROAS-driven media buying.
Responsibilities mentioned in the listing
- Build models to select the auto insurance carrier shown to each shopper on the click wall.
- Develop and own media-buying models for spend and bidding that are evaluated on ROAS.
- Put carrier quote, policy, and acquisition-cost data to work in production models.
- Define problems with business partners, identify and prepare data, build and validate models, and oversee deployment and monitoring.
- Address delayed outcomes, sparse sessions, repeat users, market drift, and the effects of bidding decisions on future training data.
- Initially focus on auto insurance, with scope broadening to additional verticals over time.
Qualifications stated in the listing
- At least five years of applied data science experience with measurable business impact.
- Experience using models to make paid-media decisions, such as budget allocation, bidding, or ad serving; attribution or reporting experience alone does not meet this requirement.
- Ability to explain work listed on your resume in depth, including loss functions, architecture choices, and neural-network fundamentals.
- Understanding of how bidding affects future training data, and experience handling late labels, sparse features, or model 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 ad budgets in live auctions; building large-scale ranking or recommendation systems; explaining technical tradeoffs to nontechnical partners; addressing auction feedback loops, selection bias, and exploration; hands-on neural-network work such as text, creative, or user-behavior embeddings; AWS data-science tools including ECS, S3, Athena, or SageMaker; and CI/CD deployment.
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.