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About the Role

Build and own predictive models for carrier selection and media spend optimization in the auto insurance vertical, with potential to expand to other verticals. This hands-on individual contributor role involves framing business problems, data sourcing, model building, validation, deployment, and monitoring, working alongside an ML engineer for production support.

Responsibilities

Build models to determine which carrier each auto insurance shopper sees on the click wall.
Optimize models to send users to the carrier most likely to convert them.
Own the buying models behind media spend, measured by ROAS.
Utilize carrier conversion data (quotes, policies, acquisition cost) in production models.
Address challenges related to late-arriving data, sparse sessions, returning shoppers, and market drift.
Shape data through bid decisions for model learning.
Frame problems directly with the business.
Find and manage own data.
Build and validate models.
Oversee model deployment and monitoring.
Focus initially on auto insurance, with scope broadening to other verticals.

Requirements

5+ years of hands-on applied data science with measurable business impact.
Experience driving paid media decisions (spend, bidding, or ad serving) with models.
Ability to explain models down to the loss function and architecture choice, including neural network fundamentals.
Experience handling late labels, sparse features, or drift.
End-to-end model ownership: data sourcing, building, deployment, and monitoring.
Expert Python and SQL skills.
Experience with real ad budget spent in live auctions.
Experience with large-scale ranking or recommendation systems.
Ability to explain tradeoffs to non-technical partners.
Experience designing for auction feedback loops, selection bias, and exploration.
Hands-on neural network work, such as embeddings of text, creative, or user behavior.
Experience with AWS for data science (ECS, S3, Athena, SageMaker).
Experience with CI/CD deployment.

Skills

Data Science
Machine Learning
Python
SQL
Model Building
Model Deployment
Model Monitoring
Media Buying Optimization
ROAS Optimization
Attribution
Reporting
Neural Networks
AWS
CI/CD

Location

Remote

Work Type

Remote-first
Full-time

Experience Level

5+ years

Salary/Compensations

$175,000 to $200,000 per year

Benefits

Profit-sharing bonus
Competitive benefits

About the Company

Launch Potato is a discovery and conversion company reaching millions monthly through brands like FinanceBuzz, All About Cookies, and OnlyInYourState.
The company connects discovery, intent, and performance across finance, travel, and entertainment brands.
Launch Potato is profitable, bootstrapped, and operates with a remote-first team across 18 countries.
The company culture values ownership of outcomes, impact over activity, and embraces constant change.
Feedback is direct and respectful.
Ordinary people make themselves extraordinary at Launch Potato.

Equal Opportunity

Launch Potato is 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.