Senior Applied Scientist, Ads Measurement Science

The Ads Measurement Science team in the Measurement, Ad Tech, and Data Science (MADS) team of Amazon Ads serves a centralized role developing solutions for a multitude of performance measurement products. We create solutions that measure the comprehensive impact of ad spend, including sales impacts both online and offline and across timescales, and provide actionable insights that enable our advertisers to optimize their media portfolios.

We leverage a host of scientific methods, approaches and technologies to accomplish this mission, including Generative AI, classical ML, Causal Inference, Natural Language Processing, and Computer Vision.

• Lead a team of scientists to innovate on state-of-the-art ads measurement solutions leveraging artificial intelligence, causal inference, natural language processing, computer vision, and large language models.
• Directly contribute to the end-to-end delivery of production solutions through careful designs and owning implementation of significant portions of critical-path code.
• Lead the decomposition of problems and development of roadmaps to execute on it.
• Set an example for others with exemplary analyses; maintainable, extensible code; and simple, effective solutions.
• Influence team business and engineering strategies.
• Communicate clearly and effectively with stakeholders to drive alignment and build consensus on key initiatives.
• Foster collaborations among scientists and engineers to move fast and broaden impact.
• Actively engage in the development of others, both within and outside of the team.
• Regularly engage with the broader science community with presentations, publications, and patents.

We are a team of scientists across Applied, Research, Data Science and Economist disciplines. You will work with colleagues with deep expertise in ML, NLP, CV, Gen AI, and Causal Inference with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, sales leaders, and other scientists with expertise in the ads industry and on building scalable modeling and software solutions.

• 5+ years of building machine learning models for business application experience
• PhD, or Master’s degree and 8+ years of applied research experience
• Experience programming in Java, C++, Python or related language
• Experience with neural deep learning methods and machine learning
• Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensor Flow, numpy, scipy etc.
• Experience with large scale distributed systems such as Hadoop, Spark etc.
• Experience with popular deep learning frameworks such as MxNet and Tensor Flow
• 5+ years of building machine learning models or developing algorithms for business application experience
• Experience in patents or publications at top-tier peer-reviewed conferences or journals

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 (Use the "Apply for this Job" box below). 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.

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USA, NY, New York -  -  USD annually