About the job
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.
Responsibilities
- Lead a team of scientists, setting the technical vision and science roadmap for ads impact measurement and optimization.
- Design experiments that identify the causal mechanisms through which advertising drives shopper engagement, advertiser value, and marketplace outcomes.
- 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.
- Lead the research and communicate findings and recommendations to senior leadership through written narratives that connect technical science to business strategy.
- 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.
- Work cross-functionally, influencing across organizational boundaries to drive alignment on complex, multi-sided tradeoffs.
Qualifications
Minimum
- 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
- 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