Applied Scientist III - Amazon Identity Program, Amazon Identity Program (AIP)

AmazonNew York City, New YorkOn-siteFull-timeMid level, 2–5 yearsListed 9 minutes ago

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About this role

Description

Advertising identity is one of the most challenging problems in modern machine learning. Amazon Advertising is looking for an Applied Scientist III to help solve it at global scale, applying advanced AI, graph neural networks, and probabilistic modeling to connect billions of identity signals across devices, publishers, and the open internet.

The Amazon Advertising Identity Platform (AIP) powers how advertisers recognize, reach, and measure audiences across the advertising ecosystem. As traditional identity signals become less available and consumer behavior spans an increasing number of devices and environments, we are investing in new scientific approaches to build a more accurate, scalable, and privacy conscious identity graph.

As a senior scientific leader, you will develop novel approaches to probabilistic identity resolution, graph neural networks (GNNs), representation learning, and large scale entity matching. You will combine deterministic identity signals from Amazon's owned and operated properties with signals from publishers, agencies, and third party identity providers to build accurate identity models across increasingly fragmented digital environments.

You will define scientific direction, design and evaluate new models, and partner closely with engineering and product teams to bring research into production. Your work will directly influence advertising addressability, audience reach, campaign measurement, and billions of dollars in advertising revenue.

The ideal candidate has deep expertise in applied machine learning, graph algorithms, and probabilistic modeling, with a demonstrated ability to translate scientific research into production systems. You should be comfortable operating in ambiguous problem spaces, challenging existing approaches, mentoring scientists, and influencing technical strategy across organizations.

This role offers the opportunity to shape the future of identity resolution at Amazon, advance the state of the art in applied machine learning, and deliver scientific innovations with measurable impact across the global advertising industry.

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

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, NY, New York - 183,800.00 - 248,700.00 USD annually