Applied Scientist II, Demand Enablement, Product Analytics and Operations

Amazon is hiring an Applied Scientist II, Demand Enablement, Product Analytics and Operations in New York, United States. It pays $172k-$223k a year and Level rates it ; you can apply on Amazon.

Level's read on this job

On Level's scale, this applied scientist role is Builds AI ●●●●: the job is building AI systems.
Pay on this posting starts in line with the typical applied scientist posting on Level.
The posting mentions RAG, computer vision and machine learning among its tools and skills.
Level classes it as a mid-level role.
Amazon has 20,000+ other open roles on Level.

AI in this role

Design and build intelligent multi-agent systems and machine learning models to automate advertising campaign troubleshooting.

ragcomputer-visionmachine-learningllmmulti-agent-systemsretrieval-augmented-generationpythondistributed-systems

In this role, you will design and build intelligent multi-agent systems that automate root cause analysis for advertising campaign delivery at scale. You will architect agentic orchestration patterns where specialized sub-agents (campaign diagnostics, deal-level troubleshooting, pacing control) are invoked as composable tools by a reasoning layer that determines which subsystems to query based on the nature of the issue. You will develop hierarchical analysis frameworks that move from daily trend detection to intra-day anomaly isolation, enabling the system to pinpoint when and why delivery degraded rather than relying on static time windows. You will build self-learning feedback loops where the system identifies recurring failure signatures (auction dynamics, pacing anomalies, supply contention), updates its diagnostic knowledge as engineering teams deploy fixes, and retires stale patterns automatically. We are looking for a passionate Applied Scientist with technical expertise in LLM-based agent architectures, retrieval-augmented generation, time-series anomaly detection, and production ML systems. In addition to hands-on experience building agentic AI solutions, an ideal candidate should demonstrate the ability to translate complex distributed system behaviors into structured diagnostic reasoning, show a willingness to push the boundaries of how LLMs interact with real-time operational data, and thrive in an environment where you ship production systems that directly reduce advertiser escalation time from days to minutes.

Key job responsibilities
* Conduct deep data analysis to derive insights for the business, identify gaps, and uncover new opportunities.
* Develop scalable and effective machine learning models and optimization strategies to solve business problems.
* Run regular A/B experiments, gather data, and perform statistical analysis to optimize advertiser experiences.
* Collaborate closely with software engineers to deliver end-to-end solutions into production.
* Enhance the scalability, efficiency, and automation of large-scale data analytics, model training, deployment, and serving.
* Research and implement new machine learning models and techniques to improve advertising performance.

A day in the life
Your primary focus is building a multi-agent diagnostic system that automates root cause analysis for advertising campaign delivery issues. On a typical day, you might review how the system handled recent escalations, identify where it reasoned incorrectly, adjust orchestration logic, and write new evaluation cases. You will design agent architectures that invoke specialized sub-agents as tools, build hierarchical analysis frameworks that move from trend detection to anomaly isolation, and develop self-learning loops that keep the system's diagnostic knowledge current as the underlying platform evolves. You will work closely with SDEs building the diagnostic platform, product managers defining the troubleshooting experience, and the support teams who rely on your system to resolve advertiser delivery issues in minutes instead of days. Beyond the core agent work, you may find yourself diving into causal inference to measure recommendation effectiveness, prototyping proactive anomaly detection, or contributing to evaluation science for systems that reason over complex operational data.

About the team
The Demand Enablement, Product Analytics and Operations team builds the diagnostic and intelligence layer for Amazon DSP, the demand-side platform powering Amazon's programmatic advertising business. We own the systems that detect, diagnose, and surface delivery issues across campaigns, giving internal teams and advertisers the visibility to act before problems impact spend. Our product portfolio spans automated troubleshooting platforms, advertiser-facing delivery insights, and AI-powered root cause analysis using multi-agent architectures on foundation models. We are a small, high-ownership team that ships production systems end-to-end, from data pipelines processing billions of bid events to LLM-based agents that reason over complex advertising systems. If you want to work at the intersection of applied science, distributed systems observability, and real business impact measured in advertiser dollars recovered, this is the team.

Basic qualifications

- 3+ years of building models for business application experience
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- 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

Preferred qualifications

- Experience developing and implementing deep learning algorithms, particularly with respect to computer vision algorithms
- Experience in professional software development
- Experience in designing experiments and statistical analysis of results

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, NY, New York - 172,400.00 - 223,400.00 USD annually
USA, WA, SEATTLE - 142,800.00 - 193,200.00 USD annually

How we rate this

Applied Scientist II, Demand Enablement, Product Analytics and Operations 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.

Classification

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. Read the description and count the AI tools, models and workflows it names.

Key facts

Applied Scientist II, Demand Enablement, Product Analytics and Operations is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $172k-$223k a year.
The role is based in US, NY, New York.
The job was posted on June 10, 2026.
Seniority: mid.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

RAGComputer visionMachine learningLLMMulti Agent SystemsRetrieval Augmented GenerationPythonDistributed Systems

Also common in applied scientist jobs at this level

Generative AI NLP Reinforcement learning TensorFlow Spark AWS

Pay

This job starts at: $172k
Typical applied scientist job: $172k

This posting starts close to the typical applied scientist job. See applied scientist jobs

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
Walk me through a computer vision problem you solved, from raw data to a deployed model.
Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where llm was part of your work. What did you do?
Tell me about a project where multi agent systems was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: RAG, Computer vision, Machine learning, LLM, and Multi Agent Systems. 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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