Senior AdTech Data Scientist - Remote-First, High-Impact ML

Launch Potato; Oregon; Hybrid; USD 175,000 - 200,000; Full time

Benefits offered by this job

Remote-first team

Profit-sharing bonus

Competitive benefits

Job summary

Launch Potato, a remote-first discovery and conversion company, seeks a data science lead to build and productionize models that optimize insurance marketplace bidding and carrier matching. You will own data-to-deployment, collaborate with ML engineers, and expand models across verticals while maintaining production quality and driving measurable impact.

The role emphasizes end-to-end ownership, expert Python/SQL, and the ability to frame business problems as data science solutions in a

Qualifications

• 5+ years of hands-on applied data science with measurable business impact.
• Your models drive 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

Responsibilities

• Build models that decide which carrier a shopper sees on our click wall to maximize conversions.
• Own the buying models behind media spend with ROAS targets.
• Work with data and production deployment, monitoring, and drift handling.
• Collaborate with an ML engineer on the production side.
• Expand from auto insurance to other verticals over time.

Skills

Data science

Python

SQL

Machine learning

Tools

AWS SageMaker

Job description

Launch Potato, a remote-first discovery and conversion company, seeks a data science lead to build and productionize models that optimize insurance marketplace bidding and carrier matching. You will own data-to-deployment, collaborate with ML engineers, and expand models across verticals while maintaining production quality and driving measurable impact.

The role emphasizes end-to-end ownership, expert Python/SQL, and the ability to frame business problems as data science solutions in a