Staff Applied Scientist, AdTech

Launch Potato; Brasil; Remoto; BRL 611.000 - 916.000; Tempo pieno

Vantaggi offerti da questo lavoro

Remote-first

Global team across 18 countries

Descrizione del lavoro

Launch Potato is seeking a senior data scientist to build models that decide which auto insurance carrier a shopper sees on our click wall, with the aim of maximizing conversions and ROAS. You will own the buying models behind our media spend and work with an ML engineer on production deployment, monitoring, and data-driven improvements.

The role emphasizes hands-on modeling, data discovery, and a strong understanding of how bidding and attribution shape future data, with broad scope into other

Competenze

• Hands-on data science with measurable business impact.
• Models used to drive paid media decisions (spend, bidding, ad serving).
• Explain loss functions and architecture choices, including neural networks.
• Understand how bids shape training data: late labels, sparse features, drift.
• Own models end to end: data discovery, deployment, monitoring, and governance.

Mansioni

• Develop models that optimize insurance carrier assignment for auto shoppers.
• Own production-ready models and monitor performance and drift.
• Coordinate data gathering and feature engineering with business needs.
• Collaborate with ML engineer on production deployment and monitoring.

Conoscenze

Python

SQL

Data modeling

Experiment design

End-to-end ML

Strumenti

AWS

SageMaker

S3

Athena

ECS

Descrizione del lavoro

WHO ARE WE?

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
• 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

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.

Voluntary Self‑Identification

For government reporting purposes, we ask candidates to respond to the below self-identification survey.Completion of the form is entirely voluntary. Whatever your decision, it will not be considered in the hiringprocess or thereafter. Any information that you do provide will be recorded and maintained in aconfidential file.

As set forth in Launch Potato’s Equal Employment Opportunity policy,we do not discriminate on the basis of any protected group status under any applicable law.

If you believe you belong to any of the categories of protected veterans listed below, please indicate by making the appropriate selection.As a government contractor subject to the Vietnam Era Veterans Readjustment Assistance Act (VEVRAA), we request this information in order to measurethe effectiveness of the outreach and positive recruitment efforts we undertake pursuant to VEVRAA. Classification of protected categoriesis as follows:

A \"disabled veteran\" is one of the following: a veteran of the U.S. military, ground, naval or air service who is entitled to compensation (or who but for the receipt of military retired pay would be entitled to compensation) under laws administered by the Secretary of Veterans Affairs; or a person who was discharged or released from active duty because of a service‑connected disability.

A \"recently separated veteran\" means any veteran during the three‑year period beginning on the date of such veteran's discharge or release from active duty in the U.S. military, ground, naval, or air service.

An \"active duty wartime or campaign badge veteran\" means a veteran who served on active duty in the U.S. military, ground, naval or air service during a war, or in a campaign or expedition for which a campaign badge has been authorized under the laws administered by the Department of Defense.

An \"Armed forces service medal veteran\" means a veteran who, while serving on active duty in the U.S. military, ground, naval or air service, participated in a United States military operation for which an Armed Forces service medal was awarded pursuant to Executive Order 12985.