Applied Science Manager, CBA

Amazon is hiring an Applied Science Manager, CBA in Seattle, United States. It pays $184k-$249k a year and Level rates it ; you can apply on Amazon.

Level's read on this job

On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
The posting mentions Python, PyTorch and computer vision among its tools and skills.
Level classes it as a lead role.
Amazon has 20,000+ other open roles on Level.

AI in this role

Lead an applied science team building transformer-based foundation models for customer behavior, causal inference, and decision-making at scale.

PythonPyTorch

computer-visionmachine-learningcausal-inferencesequence-modelingdeep-learningteam-management

Amazon is hiring a Manager to lead a science team within Customer Forecasting and Valuation (CFV). CFV owns several of the primary decision metrics Amazon uses to evaluate launches and investments — causal estimates of how customer actions today translate into customer value over the year ahead. These metrics are how Amazon works backwards from the customer at scale: they let thousands of launch decisions a year, across Retail, Ads, Marketing, and Selection, weigh short-term profitability against long-term growth.

We are in the middle of a generational rebuild of how these metrics are produced. Our team is developing transformer-based foundation models of customer behavior, learned directly from billions of behavioral events. They are being built as shared infrastructure: one learned representation of customer behavior that a wide range of measurement and optimization systems across Amazon can be built on top of.

CFV is part of the Customer Behavior Analytics (CBA) organization, which builds the tools used to understand customer behavior and value generation across Amazon's Retail business.

As a manager in CFV, you will lead Applied Scientists, Economists, and Data Scientists working at the intersection of causal inference, sequence modeling, and experimentation. The work spans methodological invention — making learned representations estimation-aware, closing the loop between experimental ground truth and model training, extrapolating short-horizon observations into year-ahead causal effects — and translation of that work into production systems that move real launch decisions. You will set multi-year research and product agendas in partnership with senior scientists, product owners, and business leaders, and you will work closely with a dedicated engineering team.

The right candidate has deep expertise in ML and causal inference, judgment about when to invent versus reuse, and the appetite to operate in ambiguity. If you want to lead a team shaping how Amazon measures the value of what it does for customers, and how it balances short-term profitability with long-term growth, we should talk.

Basic qualifications

- 3+ years of scientists or machine learning engineers management experience
- PhD, or Master's degree
- Knowledge of machine learning approaches and algorithms

Preferred qualifications

- 5+ years of building machine learning models or developing algorithms for business application experience
- Experience building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers

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 - 183,800.00 - 248,700.00 USD annually

How we rate this

Applied Science Manager, CBA 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 Science Manager, CBA is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $184k-$249k a year.
The role is based in US, WA, Seattle.
The job was posted on July 21, 2026.
Seniority: lead.
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

Computer visionMachine learningCausal InferenceSequence ModelingDeep learningTeam ManagementPythonPyTorch

Questions you could be asked

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 causal inference was part of your work. What did you do?
Tell me about a project where sequence modeling 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?

Adapt your resume

List these exact terms on your resume: Computer vision, Machine learning, Causal Inference, Sequence Modeling, 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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