AmazonApplied Scientist II, Predictive Targeting
Amazon is hiring an Applied Scientist II, Predictive Targeting in Seattle, United States. It pays $143k-$193k a year and Level rates it ; you can apply on Level.
AI in this role
Build machine learning models, reinforcement learning systems, and agentic workflows for customer marketing targeting.
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.
Basic qualifications
- 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
Preferred qualifications
- 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 https://amazon.jobs/content/en/how-we-hire/accommodations 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 https://amazon.jobs/en/benefits.
USA, WA, Seattle - 142,800.00 - 193,200.00 USD annually
How we rate this
Applied Scientist II, Predictive Targeting at Amazon rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in applied scientist jobs at this level
Pay
- This job starts at
- $143k
- Typical applied scientist job
- $167k
This posting starts 15% below the typical applied scientist job. See applied scientist jobs
Questions you could be asked
- How do you decide when an AI agent can act on its own versus asking for approval first?
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where reinforcement learning was part of your work. What did you do?
- Tell me about a project where causal inference was part of your work. What did you do?
- Tell me about a project where predictive modeling was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: AI agents, Machine learning, Reinforcement learning, Causal Inference, and Predictive Modeling. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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