Applied Scientist, Amazon Live Data Engineering, Sciences and Analytics (DESA)

About the role

Amazon Live is building the future of shoppable video — connecting brands with customers through livestreams, short-form video, and creator-driven content across Amazon Shopping, Fire TV, Prime Video, and social platforms. The product serves millions of monthly viewers, processes billions of events daily across 9 marketplaces, and generates tens of millions of dollars in advertiser revenue through self-service and managed channels with a goal to reach 100MM+ MAUs and 20K+ brands in the next couple of years.

Responsibilities

• Design and deploy causal attribution models (incrementality testing, multi-touch) replacing heuristic approaches, producing defensible numbers for partner teams and brand-facing ROI metrics.
• Build brand lifecycle models (LTV, cost-to-acquire, adoption funnel) and campaign optimization models (marketing mix, diminishing returns) that scale self-service revenue.
• Design and run A/B experiments with proper methodology (holdouts, pre-registration, power analysis) for new product surfaces, ranking changes, and attribution model transitions.
• Build multimodal and generative models for content intelligence — extracting structured signals from video and producing scored creative assets at scale.
• Develop ranking and personalization features (content affinity, creator quality indices, cross-session engagement patterns) consumed by downstream distribution systems.
• Build predictive models proving video value to Amazon's programmatic systems where existing retail signals fail.
• Own the full lifecycle from research question through production deployment, monitoring, and iteration.
• Present findings and methodology to senior leadership (Director/VP) and partner teams, translating model outputs into business decisions.
• Contribute to the science community through internal publications, reading groups, and cross-team methodology sharing.

Requirements

• 3+ years of building models for business application experience
• PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
• Experience programming in Java, C++, Python or related language
• Experience working with PyTorch or JAX software, or experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution
• Experience with A/B testing, especially around audience segmentation and targeting
• Experience with large scale distributed systems such as Hadoop, Spark etc.
• Experience with causal inference methods (incrementality testing, difference-in-differences, instrumental variables, or synthetic control)

Qualifications

• Experience in search advertising, search marketing, performance advertising, or similar digital advertising
• Experience with video and image processing and compression algorithms and standards, computer vision and/or machine learning
• Experience in a marketing focused role including customer lifecycle marketing, segmentation reporting, customer funnel analysis, and top-line sales performance
• Track record of deploying models that directly influenced product decisions or business strategy
• Experience with Amazon internal tools (Bedrock, SageMaker, Redshift, Cradle) is a plus but not required