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. Amazon Live has a mature data platform powering reporting across 9 marketplaces, 14+ dashboards, and real-time creator analytics. The opportunity ahead is different: proving the causal value of video to brands, Amazon's programmatic systems, and product decision-making. This requires production ML models, data experimentation frameworks, and content intelligence that do not exist today - and that is exactly what this role builds.

You will be one of the first scientists on this team - defining the measurement methodology, experimentation standards, and model architecture from the ground up. The problems are high-ambiguity, the data is rich, and the impact is visible - your models will directly influence how brands invest and how millions of customers discover content. Are you excited by the challenge of building causal measurement, content intelligence, and ranking signals from scratch for a product customers interact with daily? Do you want to own the full lifecycle from research question through production deployment, where your work moves multi-million dollar business decisions?

We are looking for an Applied Scientist to join the DESA team and build production-grade models, experiments, and signals that close these gaps. You will own problems end-to-end - from framing the research question through model deployment and A/B experimentation - working alongside Data Engineers who build the infrastructure and a BIE who owns executive reporting. Your outputs will not sit in notebooks. They will run in production, feed downstream ranking systems, power brand-facing metrics, and give partner teams the evidence they need to prioritize integrations with Amazon Live.

Key job 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.

We value builders who thrive in ambiguity, move fast from hypothesis to production, and measure their success by business outcomes rather than paper count.

A day in the life

You will partner directly with product managers, monetization leads, and engineering peers to scope what to measure and how to prove it. Some weeks you will be designing an incrementality framework for ad lift and presenting methodology to leadership. Other weeks you will be training a multimodal model on broadcast video, packaging ranking features for the distribution team, or developing an A/B experiment for a new video produce on a new discovery surface. You will rapidly prototype and test hypotheses in a high-ambiguity environment, making use of both quantitative analysis and business judgment. You will work on DE-built foundational data assets (content metadata, shopper profiles, retail/advertising integrations) and have access to a self-serve analytics agents and agentic-operational tooling that accelerate exploration. You will present findings to senior leadership (Director/VP level) and partner teams, turning data into prioritization decisions.

About the team

The DESA team owns the data platform, analytics, and sciences for Amazon Live and Shop the Show. The team's mission: quantify the value of live video to Amazon's ecosystem and make that value programmable - for brands deciding where to invest, for product teams deciding what to build, and for Amazon's systems deciding what to show customers. You will be one of the first scientists on a team that has built the data foundation and is now ready to build the intelligence layer on top.

The platform processes event data from internal Amazon systems and social surfaces across 9 marketplaces. DEs own the infrastructure and foundational data assets. The BIE owns executive reporting. Applied Scientists own the models, experiments, and signals that turn raw data into product value and business decisions. Your outputs become shared org infrastructure - attribution models for partner teams, ranking features for distribution, experiment methodology for product, and content intelligence for creative supply.

BASIC QUALIFICATIONS

- 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).

PREFERRED 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

- Experience working with cross-functional teams across business development, marketing, operations, product development, legal, etc.

- 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.

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, WA, SEATTLE - 142,800.00 - 193,200.00 USD annually