AmazonPosted 1d ago

Applied Scientist III, Demand Technology, Amazon Demand Side Platform, Bravo Non-endemic

Applied Scientist III, Demand Technology, Amazon Demand Side Platform, Bravo Non-endemic at Amazon scores 90 out of 100 on AI centrality, which makes it a Level 4 role on this board.

US, NY, New Yorkseniorfull-time$184k-$249k

AI in this role

Own the applied science roadmap and machine learning models for conversion prediction, ranking, and measurement on the Demand Side Platform.

tensorflowscikit-learn

machine-learningcausal-inferenceexperiment-designpythonmodeling

Amazon's Demand Side Platform (DSP) helps advertisers reach audiences across the web, and we are tackling one of the hardest open problems in this space: making performance advertising work for non-endemic advertisers — brands in financial services, telco, auto, travel, and direct-to-consumer that sell outside of Amazon. As an Applied Scientist III on the Demand Technology team, you will own the science behind conversion prediction, bidding, ranking, and measurement for these advertisers, where standard signals are sparse and new modeling approaches must be invented. This is a company-level priority with significant room for scientific impact, and the models you build will directly determine whether advertisers see the outcomes that keep them investing on our platform.

Key job responsibilities
- Own the applied-science roadmap for one or more non-endemic performance workstreams — from problem framing and experiment design through offline validation, online experimentation, and production launch.

- Design and improve machine learning models for sourcing, ranking, and response prediction that optimize toward advertiser outcomes such as cost per acquisition and return on ad spend.

- Define measurement methodology — including incrementality, weighted conversions, and off-Amazon attribution — that correctly values an impression when the conversion happens outside Amazon.

- Partner with engineering, product, and sales-facing teams on deep dives into real enterprise advertiser accounts, turning account-level learnings into scalable model and system improvements.

- Mentor scientists and engineers across the organization, publish internal best practices, and contribute to the broader scientific community through peer reviews and publications.

A day in the life
You might start your morning analyzing offline experiment results for a new conversion-prediction model, then join a design review with engineers on how to serve that model in the real-time bidding pipeline. After lunch launch, you could be working with a product manager to define success metrics for a non-endemic advertiser segment, followed by a code review for a teammate's feature-engineering change. You regularly carve out time to read recent research on causal inference or data-efficient learning and assess whether new techniques could improve your team's models.

About the team
We are a lean team of applied scientists and software development engineers within Amazon DSP, focused on strategic initiatives that have not had significant investment before. We work close to the customer — partnering directly with product and sales-facing teams to understand advertiser needs and translate them into scalable scientific solutions. If you want to shape the direction of a high-priority problem space where your ideas move quickly from whiteboard to production, we would love to hear from you.

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.

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 - 183,800.00 - 248,700.00 USD annually

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Skills and AI tools this role asks for

Machine LearningCausal InferenceExperiment DesignPythonModelingTensorFlowscikit-learn

Questions you could be asked

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 experiment design was part of your work. What did you do?
Tell me about a project where python was part of your work. What did you do?
Tell me about a project where modeling was part of your work. What did you do?

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