AmazonPosted 2d ago

Applied Scientist II, Partner Science

Applied Scientist II, Partner Science at Amazon scores 95 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.

US, WA, Seattlemidfull-time$172k-$223k

AI in this role

Design and productionize machine learning, AI, and causal inference models to optimize Amazon Ads' partner ecosystem.

sagemakerpythonpytorchllms

ragml-opsmachine-learningcausal-inferencedeep-learningeconometricstext-analytics

Amazon Ads is building a >$100BN business, and our 3000+ advertising partners — agencies and tech providers — are strategic growth engines for that ambition. The Partner Science team drives the Advertising Partner flywheel by infusing science-based interventions at every stage of the partner journey: demand generation, partner selection, partner engagement and growth, and partner value, and partner experience measurement.

We are looking for an Applied Scientist to join our team and develop ML/AI models and causal inference studies that directly improve how advertisers find, work with, and succeed through partners. In this role, you will design, build, and productionize ML/AI and econometric solutions. You will work on ambiguous, real-world and high-impact problems where neither the problem nor the solution is well-defined, and you will be trusted to operate with growing autonomy while collaborating closely with senior and principal product managers, engineers, data engineers, BIEs, and sales/marketing stakeholders.

Key job responsibilities
• Design, prototype, validate, and productionize ML models across science domains: Predictive/Supervised (e.g., propensity models, deep learning, reinforcement learning), Causal Measurement (A/B tests, causal inference studies), and Text Analytics/LLMs (signal extraction, model explainability, Gen-AI application).
• Independently own one or more production science models end-to-end— from initial scoping and design, to final deployment and ongoing monitor and refinement. Conduct scientific literature reviews, benchmark state-of-the-art approaches, and develop novel techniques when no textbook solution exists for our partner ecosystem challenges
• Design and run A/B experiments using our scalable experiment framework to validate whether science interventions and product features drive partner growth and ad spend, working with our small, skewed partner population
• Perform hands-on data analysis with large-scale advertising datasets, leveraging our centralized Partner Knowledge Base with hundreds of numeric features and unstructured data and Andes data infrastructure Collaborate with the MLOps engineering team to deploy models, and with Data Engineering to create curated datasets that power science and analytics efforts
• Translate model outputs into business impact for cross-functional stakeholders (Different Sales and Marketing teams, Finance, Partner Product teams), simplify and provide business friendly communication to drive effective debates and trade-off discussion, and lead to alignment and decision.
• Contribute to model quality monitoring, data quality frameworks, and operational excellence — including defining evaluation thresholds and data quality checks at each pipeline stage
• Mentor teammates and contribute to a culture of intellectual integrity, continuous learning, and knowledge sharing

About the team
The Partner Science team sits within the Partner Analytics organization in PartnerTech, Amazon Ads. Our mission is to drive the Advertising Partner flywheel by infusing science-based interventions at all stages of the partner journey — demand generation, partner selection, partner engagement and growth, and partner value — ultimately improving the partner-managed advertiser experience.

We are part of a broader Partner Analytics team comprising Data Engineering, Business Intelligence, and Science functions, all unified by a shared commitment to both advertiser and partner success. The Science team currently includes senior applied scientists, data scientists, supported by MLOps engineering partners who help us scale model deployment. Together, we own 10+ production science models and studies that power Partner Network platform features, sales and marketing programs, and finance attribution and forecasting across 20+ marketplaces.

We bias for action, embrace a culture of fast iteration and reinforcement learning, celebrate both achievements and lessons learned, and invest in growing top scientist talent. If you are enthusiastic about applying ML/AL, causal inference, and LLMs to real-world advertising ecosystem problems with measurable business impact, we'd love to hear from you. Too learn more about us, see our wiki https://w.amazon.com/bin/view/AdSales/SPE/PEG/Analytics/Science/Overview

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
- 3+ years of hands-on predictive modeling and large data analysis experience

Preferred qualifications

- Experience in professional software development
- Ph.D. in computer science, machine learning, engineering, or related fields, or experience in data science, machine learning or data mining
- Knowledge of data engineering pipelines, cloud solutions, ETL management, databases, visualizations and analytical platforms
- Experience completing complex tasks quickly with little to no guidance and react with appropriate urgency to situations that require a quick turnaround, or experience in sales/business development
- • 5+ years experience building propensity models, recommendation systems, or advertiser/partner targeting models in an advertising or marketplace context
- • Depth and hands on design experience on A/B test and causal and economic impact analysis. Hands-on experience with LLMs and Gen-AI applications (e.g., text summarization, retrieval-augmented generation, digital assistants)
- • Experience with AWS data and ML infrastructure (e.g., SageMaker, S3, Redshift, Athena, Andes, Batch) or equivalent cloud-based ML platforms
- • 3+ years experience working in advertising and retail ecosystem, and has done global level projects to understand customer behaviors
- • Track record of mentoring peers or contributing to the scientific community through publications, internal conference or tech talks, or knowledge sharing

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

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

RagMl OpsMachine LearningCausal InferenceDeep LearningEconometricsText AnalyticsSagemaker

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
How do you monitor a model once it's live, and how do you know it needs retraining?
Tell me about a project where machine 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 deep learning was part of your work. What did you do?

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List these exact terms on your resume: Rag, Ml Ops, Machine Learning, Causal Inference, and Deep Learning. 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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