Lead Data Scientist, AdTech

Launch Potato • San Jose, CA

13d • Remote

About The Position

Launch Potato is seeking a hands-on individual contributor to build and operate data science models for its auto insurance marketplace and media buying. The role will develop models that select the carrier most likely to convert each shopper on the click wall, and buying models that guide media spend against return on ad spend (ROAS). The work will put returning carrier conversion data—including quotes, policies, and acquisition cost—into production models.

The role addresses delayed outcomes that may arrive weeks or months after a click, sparse sessions, repeat shoppers, changing market conditions, and feedback loops in which bidding decisions influence which users are acquired and therefore the data available for future training. The scientist will frame problems with business partners, source and analyze data, build and validate models, and own them through deployment and monitoring, working alongside an ML engineer on production implementation. Auto insurance will be the initial focus, with scope expected to broaden to other verticals over time. The company is remote-first, and most employees work East Coast hours.

Success includes independently running insurance marketplace media buying by day 60; building click-wall serving that matches users to carriers; using carrier conversion data in production models; scaling automated marketplace bidding; establishing approaches for repeat users and sparse sessions; and delivering reliable, trusted production models that adapt to drift and changing objectives.

Requirements

• At least 5 years of hands-on applied data science experience with measurable business impact.
• Experience building models that drove paid media decisions, such as spend, bidding, or ad serving; attribution and reporting alone do not qualify.
• Ability to explain personal modeling work in depth, including loss functions, architecture choices, and neural network fundamentals.
• Understanding of how bids affect future training data, with experience handling late labels, sparse features, or drift.
• Experience owning models end to end, including finding data, building models, and carrying them through deployment and monitoring.
• Expert Python and SQL.

Nice To Haves

• Experience spending real ad budgets in live auctions.
• Experience with large-scale ranking or recommendation systems.
• Ability to explain tradeoffs to non-technical partners in plain language.
• Experience designing for auction feedback loops, selection bias, and exploration.
• Hands-on neural network experience, such as creating embeddings of text, creative, or user behavior.
• AWS data science experience with ECS, S3, Athena, or SageMaker.
• CI/CD deployment experience.

Responsibilities

• Build models that select the auto insurance carrier most likely to convert each shopper on the click wall.
• Own media-buying models that guide spend and bidding against ROAS.
• Use carrier conversion data, including quotes, policies, and acquisition cost, in production models.
• Address delayed labels, sparse sessions, repeat users, market drift, and feedback loops between bidding decisions and training data.
• Frame problems directly with business partners and independently find and prepare relevant data.
• Build and validate models, then own their deployment and monitoring in collaboration with an ML engineer.
• Run insurance marketplace media buying independently by day 60.
• Develop production approaches for repeat users and sparse sessions, and scale automated marketplace bidding.
• Broaden the initial auto insurance work to other verticals over time.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed