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

- **Company:** Amazon.com, Inc.
- **Location:** Seattle, WA, United States
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Signals Intelligence, Information Engineering, Statistical Hypothesis Testing, Software Deployment, Power Analysis (Cryptography)
- **Published:** October 31, 2026
- **Apply:** https://www.careerbuilder.com/job-details/applied-scientist-amazon-live-data-engineering-sciences-and-analytics-desa-seattle-wa--026db326-c35a-4f1d-9ec5-8d9b0d638a54

## About the Role

Consumer Branding, Content Ranking, Signal Intelligence (SIGINT), Establish Priorities, Metrics, Return on Investment (ROI), LifeTime Value (LTV), Campaigns, Marketing, Analysis Skills

## Description

* 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.