Job description

Are you passionate about using data and machine learning to improve how Amazon reaches customers? The Customer Targeting team is looking for an Applied Scientist to build the science behind the targeting of Amazon's marketing initiatives across channels and ad formats. You will turn business questions into modeling problems and take your solutions from prototype to production, working closely with senior scientists, engineers, and business partners. We are moving targeting toward outcome-based optimization and an AI-native, agentic way of working. In this role you will work on online optimization and reinforcement learning, incrementality modeling, and the next generation of our segment-based targeting model. You will also help build agentic workflows that make targeting decisions transparent and bring science recommendations to the people using them. The right candidate has strong quantitative and statistical skills, hands-on experience with modern ML methods, and is comfortable working through ambiguity in a fast-paced environment. You pay close attention to detail, learn quickly, and work well in a highly collaborative team. Key job responsibilities - Build online optimization (e.g., TSO, bandits) and reinforcement learning methods that optimize targeting decisions for business outcomes rather than proxy metrics. - Develop incrementality and uplift models that estimate the causal impact of targeting, and use them to improve who we target and how much we spend. - Improve the existing segment-based targeting model: engineer and evaluate new features, migrate it to new model architectures, and build automated feedback loops that retrain and improve the model from observed outcomes. - Contribute to the intelligence layer of our agentic system: build components that explain targeting decisions and propose science-driven solutions to business partners. - Design and analyze experiments (A/B tests, causal inference methods) to measure the incremental impact of targeting changes. - Write production-quality model code and partner with engineers to deploy, monitor, and maintain models. - Present methods and results clearly, backed by data, with actionable recommendations for science and business stakeholders. About the team Predictive Targeting builds the systems that decide who sees Amazon's marketing content, and how often, across Amazon's stores and channels. We are engineers and scientists working on one shared problem: making sure customers see the most relevant and engaging marketing, and making each campaign better than the last. - 3+ years of building models for business application experience - Experience in patents or publications at top-tier peer-reviewed conferences or journals - Experience programming in Java, C++, Python or related language - Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing - PhD, or Master's degree and 3+ years of applied research experience - Experience using Unix/Linux - Experience in professional software development - Experience creating processes and improving tools that have measurably improved productivity, quality, or advertiser experience - PhD in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science, or experience in defining and creating benchmarks for assessing GenAI model performance - Experience with reinforcement learning or online optimization in production, such as multi-armed or contextual bandits, exploration vs. exploitation strategies, or real-time pacing and allocation under budget and frequency constraints. Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit amazon.jobs for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at amazon.jobs . USA, WA, Seattle - 142,800.00 - 193,200.00 USD annually

Requirements

Must-haves

Key job responsibilities
Build online optimization (e.g., TSO, bandits) and reinforcement learning methods that optimize targeting decisions for business outcomes rather than proxy metrics.
Develop incrementality and uplift models that estimate the causal impact of targeting, and use them to improve who we target and how much we spend.
Improve the existing segment-based targeting model: engineer and evaluate new features, migrate it to new model architectures, and build automated feedback loops that retrain and improve the model from observed outcomes.
Contribute to the intelligence layer of our agentic system: build components that explain targeting decisions and propose science-driven solutions to business partners.
Design and analyze experiments (A/B tests, causal inference methods) to measure the incremental impact of targeting changes.
Write production-quality model code and partner with engineers to deploy, monitor, and maintain models.
Present methods and results clearly, backed by data, with actionable recommendations for science and business stakeholders.
About the team
3+ years of building models for business application experience
Experience in patents or publications at top-tier peer-reviewed conferences or journals
Experience programming in Java, C++, Python or related language
Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
PhD, or Master's degree and 3+ years of applied research experience
Experience using Unix/Linux
Experience in professional software development
Experience creating processes and improving tools that have measurably improved productivity, quality, or advertiser experience
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
USA, WA, Seattle - 142,800.00 - 193,200.00 USD annually

Nice-to-haves

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Application prep

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Keywords to mirror

machine learningaimljavapython

Platform: amazon
Effort: medium

Benefits

Benefits detected in the listing.

Health Insurance

Dental

Vision

401(K)

Paid Time Off

Parental Leave