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Sr. Applied Scientist, DemandTech - AdDelivery, Experience and Measurement

Sr. Applied Scientist, DemandTech - AdDelivery, Experience and Measurement at Amazon scores 8 out of 100 on AI centrality, which makes it AI Level 1 of 4 (Little AI) on this board. The level measures how much of the work is AI, not seniority.

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

AI in this role

tensorflowscikit-learn

fine-tuning

Want to work on building a Amazon Ads billion dollar business, innovate on a new product, and have a positive impact on millions of views while working with industry-leading technologies? We're growing a team to support the Sponsored Ads business that powers the advertising experience for millions of viewers and advertisers daily.

Amazon is investing heavily in building a world-class advertising business and developing a collection of self-service performance advertising products that drive discovery and sales. We deliver billions of ad impressions and millions of clicks daily and are constantly challenging ourselves to create world-class products and an unparalleled shopping experience for our hundreds of millions of customers worldwide.

Key job responsibilities
We are building the next-gen smart ads campaign. At its core is an Intelligence Flywheel — an architecture where every component's output is designed to train models that improve every other component. The Model Layer that is meant to power every capability currently has no dedicated science ownership.

As the Senior Applied Scientist on this team, you own the science that makes the flywheel turn. You will turn static, threshold-based logic into self-improving, closed-loop intelligence, and define the decision policies that let the system act autonomously with advertiser trust. Concretely, you will:
* Build predictive issue-detection models that identify under-delivery, over-delivery, and performance degradation from campaign signals before they materially impact advertisers.
* Design the intervention-selection policy — which autonomous action to take — framed as a contextual bandit: choose, observe, update. Engineers implement action execution; you define the policy that selects actions.
* Establish causal attribution for autonomous optimization, separating the effect of our interventions from organic performance change, so improvements can be attributed and advertiser trust in autonomy can be earned.
* Integrate and adapt cross-model signals. Combine creative-quality, product-relevance, and budget signals into a unified advertiser-intelligence picture, and adapt general-purpose partner models to our product reality via fine-tuning, re-ranking, or thin adaptation layers.
* Close recommendation and grading feedback loops — define reward schemas for accept/reject and performance-vs-baseline signals, correlate creative-quality scores with real campaign outcomes, and feed empirical findings back to both our product and partner science teams.

You will work backwards from ambiguous business problems, set the science roadmap for the Model Layer, and partner closely with the team's software engineers — who own the services, pipelines, and execution infrastructure — so that model artifacts you produce are deployed and served in production. This is a high-leverage, high-autonomy role: your outputs are consumed by multiple engineering workstreams at once, and you set the abstractions the team builds on.

About the team
We are focused on goal-oriented, AI powered workflows that help advertisers achieve their marketing objectives. We collect campaign goals, surface relevant data at key decision points, and provide reporting that validates decision-making. Our product suite guides advertisers in building campaigns with optimal targeting, creative formats, inventory, and bid models that are highly likely to hit their goals — reducing the need for manual intervention.

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.
- Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability

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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Fine TuningTensorFlowscikit-learn

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Walk me through fine-tuning a model: what data did you use, and how did you check the result?
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