Sr Applied Scientist, WW Prime , WW Prime - AI/ML Science

AI in this role

tensorflowscikit-learnsagemaker

fine-tuning

Interested in modeling and understanding customer behavior through machine learning, artificial intelligence, and data mining over TB scale data with huge business impact on millions of customers? Join our team of Scientists developing models to model customer behavior and optimize the customer experience with Amazon Prime. This includes understanding who our customers are, long-term value of the Prime membership program, and creating the right personalized framework for content and subscription optimization. As an AI/ML expert, you will partner directly with product owners to intake, build, and directly apply your modeling solutions.
There are numerous scientific and technical challenges you will get to tackle in this role, such as optimizing/fine-tuning GenAI/LLM solutions for Prime personalization, building GenAI foundation models, global scalability of models, combinatorial optimization, cold start problem, accelerated experimentation, short/long term goals modeling, and multi-step optimization leading to reinforcement learning of the customer journey. We employ techniques from GenAI/LLMs, supervised/semi-supervised learning, deep learning, transformer architectures, using outcomes from causal Econometric modeling, and Reinforcement learning.
As the central science team within Prime, our expertise gets routinely called upon to weigh in on a variety of topics. We also emphasize the need and value of scientific research and have developed a strong publication and patent record (internally/externally) which you will be a part of.
You will also utilize and be exposed to the latest in ML technologies and infrastructure: AWS technologies (EMR/Spark, Sagemaker, DynamoDB, S3, ClaudeCode), various AI/ML algorithms and techniques (Deep Learning, GenAI/LLMs, transformers, supervised/unsupervised/semi-supervised/reinforcement learning), and statistical modeling techniques.

Stay abreast of current literature in the field and advance/build novel science solutions leveraging SoTA solutions.
Build and develop AI/ML models and supporting infrastructure at TB scale, in coordination with software engineering teams.
Leverage Deep Learning and GenAI solutions for building foundation models and personalized optimization solution.
Develop offline policy estimation tools and integrate with measurement systems/econometric models.
Establish scalable, efficient, automated processes for large scale data analyses, science development, science validation and model implementation.
Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes.
Work closely with the business to understand their problem space, identify the opportunities and formulate the problems.
Use AI/machine learning, data mining, statistical techniques and others to create actionable, meaningful, and scalable solutions for the business problems.
Design, develop and evaluate highly innovative models and statistical approaches to understand and predict customer behavior and to solve business problems.

Key job responsibilities
Stay abreast of current literature in the field and advance/build novel science solutions leveraging SoTA solutions.
Build and develop AI/ML models and supporting infrastructure at TB scale, in coordination with software engineering teams.
Leverage Deep Learning and GenAI solutions for building foundation models and personalized optimization solution.
Develop offline policy estimation tools and integrate with measurement systems/econometric models.
Establish scalable, efficient, automated processes for large scale data analyses, science development, science validation and model implementation.
Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes.
Work closely with the business to understand their problem space, identify the opportunities and formulate the problems.
Use AI/machine learning, data mining, statistical techniques and others to create actionable, meaningful, and scalable solutions for the business problems.
Design, develop and evaluate highly innovative models and statistical approaches to understand and predict customer behavior and to solve business problems.

Basic qualifications

- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning

Preferred qualifications

- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.

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 - 167,100.00 - 226,100.00 USD annually

How we rate this

Sr Applied Scientist, WW Prime , WW Prime - AI/ML Science at Amazon rates 98 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. Open the description and count.

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

Fine TuningTensorFlowscikit-learnSagemaker

Questions you could be asked

Walk me through fine-tuning a model: what data did you use, and how did you check the result?
Walk me through how you've used TensorFlow in your day-to-day work.
What are the limits of scikit-learn that you've run into, and how did you work around them?
What's a project where you used Sagemaker hands-on?
How would you decide a model or AI system is ready to ship?

Adapt your resume

List these exact terms on your resume: Fine Tuning, TensorFlow, scikit-learn, and Sagemaker. 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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