AmazonApplied Scientist III, Sponsored Products
AI in this role
In this role, you will develop rigorous methods to understand how traffic sources, customer journeys, and onsite experiences influence purchasing and revenue. You will use experimentation, causal inference, statistical analysis, and machine learning to distinguish correlation from true business impact and identify the drivers of performance changes. You will work with product, engineering, and business partners to translate complex findings into clear recommendations, design measurable interventions, and build tools that support faster diagnosis and better investment decisions. If you are energized by solving complex challenges at the intersection of AI and measurement science, we would love to talk to you.
Key job responsibilities
- Develop methods to understand how traffic sources, customer journeys, and onsite experiences drive purchasing and revenue, using experimentation, causal inference, and machine learning to separate correlation from true business impact.
- Drive or heavily influence the design of scientifically complex software solutions or systems, taking ownership of components and providing system-wide design guidance for both new and evolving systems.
- Define a long-term science vision and roadmap, combining science leadership, technical depth, and business understanding.
- Translate complex findings into clear recommendations for product, engineering, and business partners, and build tools that support faster diagnosis and better investment decisions.
- Mentor junior scientists, raise the scientific bar across the team, and identify opportunities where generative AI can accelerate learning and efficiency.
A day in the life
You might start your morning reviewing anomaly detection outputs to understand a recent shift in traffic patterns, then move into a working session with engineers to refine how an attribution model is served in production. After lunch, you could lead a design review for a new quasi-experimental framework with product and finance partners. Later, you might pair with a junior scientist on their causal inference approach, helping them sharpen their methodology before presenting results to leadership.
About the team
The Sponsored Products and Brands team builds solutions that extend advertising campaigns beyond the Amazon store, reaching shoppers across third-party websites and apps where they search and shop. We pair large-scale, low-latency systems with advanced machine learning to deliver high-quality sponsored experiences to advertisers and shoppers alike.
Within this team, the measurement program is a growing area of investment focused on understanding how shoppers arrive at Amazon and how that traffic translates into advertiser and business results. This is a high-visibility role where your work will directly inform investment decisions and product strategy. We are a distributed team of scientists, engineers, and product managers who partner closely with other cross-functional teams across the organization. If you want to shape how Amazon measures and improves the customer journey, this is the team to join.
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, NY, New York - 183,800.00 - 248,700.00 USD annually
USA, WA, SEATTLE - 167,100.00 - 226,100.00 USD annually
How we rate this
Applied Scientist III, Sponsored Products at Amazon rates 3 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.
Little AI. AI is not part of the work.
- ●●●● 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
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Skills and AI tools this role asks for
Questions you could be asked
- How do you think about the risk of an AI system in this kind of role failing silently?
- 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?
Adapt your resume
- List these exact terms on your resume: AI Safety, TensorFlow, and scikit-learn. 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.
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