Staff Applied Scientist, AdTech

launchpotato; United States; Remote; USD 150,000 - 230,000; Full time

Benefits offered by this job

Remote-first

Job summary

Launch Potato is seeking a Lead Applied Data Scientist to own the end-to-end modeling lifecycle for a priority advertising vertical, with initial focus on ROAS in insurance and advertiser quality. This hands-on role blends modeling with direct stakeholder collaboration across teams.

You will work in a remote-first environment with international teams, delivering production models and optimizing media performance while expanding scope over time.

Qualifications

• At least five years in a hands-on applied data science role with measurable business results.
• Experience in digital or performance marketing, or lead generation.
• Multiple years building and deploying machine learning solutions on AWS.
• Expert-level Python and SQL skills, with strong modeling fundamentals.
• Practical experience with multi-armed bandits or reinforcement learning, recommendation and ranking, funnel optimization, and lifetime value modeling.
• Ability to frame work around business outcomes, collaborate with engineering partners, and follow models through live performance.

Responsibilities

• Identify business opportunities, define measurable objectives, and select modeling approaches suited to them.
• Build and validate models for media buying, lead quality, customer acquisition, and monetization.
• Partner with machine learning engineers on production delivery, then stay engaged through monitoring and performance analysis.
• Improve lead quality across consumer content, messaging, and funnel experiences, tracking effects on revenue and media efficiency.
• Maintain positive ROAS while balancing acquisition performance with lead quality.
• Document and validate work, communicate progress proactively, and surface new modeling opportunities.

Skills

Python

SQL

AWS

Performance marketing

Reinforcement learning

Multi-armed bandits

Funnel optimization

Lifetime value modeling

Tools

AWS

Job description

Role overview

Lead applied data science for a priority advertising vertical, owning the path from business question and model design through deployment monitoring and commercial performance. The initial focus is insurance and advertiser quality, with scope expected to expand; return on ad spend (ROAS) is the primary measure of success. This is a hands‑on staff‑level role that combines detailed modeling work with direct stakeholder partnership.

Responsibilities

• Identify business opportunities, define measurable objectives, and select modeling approaches suited to them.
• Build and validate models for media buying, lead quality, customer acquisition, and monetization.
• Partner with machine learning engineers on production delivery, then stay engaged through monitoring and performance analysis.
• Improve lead quality across consumer content, messaging, and funnel experiences, tracking effects on revenue and media efficiency.
• Maintain positive ROAS while balancing acquisition performance with lead quality.
• Document and validate work, communicate progress proactively, and surface new modeling opportunities.

Requirements

• At least five years in a hands‑on applied data science role with measurable business results.
• Experience in digital or performance marketing, or lead generation.
• Multiple years building and deploying machine learning solutions on AWS.
• Expert‑level Python and SQL skills, with strong modeling fundamentals.
• Practical experience with techniques such as multi‑armed bandits or reinforcement learning, recommendation and ranking, funnel optimization, and lifetime value modeling.
• Ability to frame work around business outcomes, collaborate with engineering partners, and follow models through live performance.

Nice to have

• Experience applying advanced machine learning where paid digital media is central to the business.
• Creative or search embeddings, ad ranking, or models using advertising platform data.
• Insurance experience, or applying large language models and deep learning to personalization or content.
• Familiarity with Looker.

Benefits and work setup

The organization describes itself as remote‑first, with team members across multiple countries. Compensation includes a market‑based salary, profit‑sharing, and benefits; future increases are tied to company and individual performance.