Applied Science Manager, Stores-Ads Science

Amazon's Stores-Ads Science team operates at the intersection of Amazon's Stores and advertising businesses. We develop causal measurement systems, optimization algorithms, and machine learning models that inform how advertising affects shopper engagement, driving selling partner growth and marketplace economics. Our science shapes decisions both at the strategic level and in production systems. We are a team of interdisciplinary scientists who combine causal inference, economic modeling, and machine learning to drive measurable business impact.

We are looking for an Applied Science Manager to lead our Ads Impact initiative. This team owns the science of understanding and optimizing how advertising creates value for shoppers and selling partners. What makes this role distinctive is its position at the frontier of AI and Economics: as Amazon's shopping experience evolves from traditional search toward LLM-powered, agentic commerce, the fundamental mechanisms through which advertising creates value are changing. This role will partner with leading scientists and academic researchers to measure these effects through large-scale causal experimentation, and develop novel methods to encode causal and economic reasoning into AI systems that optimize the shopping experience.

Key job responsibilities
In this role, you will lead a team of scientists, setting the technical vision and science roadmap for ads impact measurement and optimization. You will design experiments that identify the causal mechanisms through which advertising drives shopper engagement, advertiser value, and marketplace outcomes. You will develop optimization algorithms that integrate these causal signals into production and business decision-making, in close partnership with engineering and product teams across the organization. You will lead the research and communicate findings and recommendations to senior leadership through written narratives that connect technical science to business strategy.

This role requires deep expertise in causal inference and experimental design, combined with strong applied ML skills and the engineering judgment to translate research into production systems. You will hire and develop future science leaders, think strategically, set ambitious roadmaps in highly ambiguous problem spaces, and foster a culture that values both intellectual depth and production impact. You will work cross-functionally, influencing across organizational boundaries to drive alignment on complex, multi-sided tradeoffs.

BASIC QUALIFICATIONS

- 4+ years of applied research experience
- 3+ years of scientists or machine learning engineers management experience
- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Knowledge of ML, NLP, Information Retrieval and Analytics
- Experience programming in Java, C++, Python or related language

PREFERRED QUALIFICATIONS

- Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution
- Experience in causal modeling like graphical models, causal Bayesian network, potential outcomes, A/B testing, experiments, quasi-experiments, and data science workflows

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