Sr. Research Scientist, Community Operations

Amazon • Arlington, VA

2d

About The Position

Amazon's Community Intelligence Science & Engineering (CISE) team operates at
the intersection of computational social science, artificial intelligence, and
operational planning. We build and validate models that quantify how Amazon's
operational presence impacts local communities, and embed those signals into
core planning models that drive Last Mile, Middle Mile, and site-level decisions
across our network. You'll work at the forefront of applied AI and causal
inference, building systems that influence decisions affecting thousands of
communities daily. This role offers a unique opportunity to shape how the
world's most customer-centric company measures, forecasts, and reduces
operational risk to the communities we serve.

A successful Senior Research Scientist on our team demonstrates exceptional
scientific rigor combined with pragmatic execution. You will design the causal
frameworks and predictive models that systematically embed these community
signals into core operational planning processes where they do not yet exist.
This means balancing breakthrough research with production deployment,
collaborating across multiple science organizations, and translating complex
analytical findings into actionable insights that influence senior leadership
strategy.

As a Senior Research Scientist, you will partner with product teams, operational
scientists, and technical leaders across Last Mile Planning Science, Middle Mile
Relay, Safety, and Worldwide Operations Security to identify opportunities where
community intelligence creates multiplicative business impact.

Requirements

• 3+ years of investigating the feasibility of applying scientific principles and concepts to business problems and products experience
• PhD, or Master's degree and 5+ years of quantitative field research experience
• Experience with big data technologies such as AWS, Hadoop, Spark, Pig, Hive etc.
• Knowledge of quantitative approaches (e.g., t-tests, regressions, ANOVAs, etc.)
• Knowledge of AWS platforms such as S3, Glue, Athena, Sagemaker
• Experience in standard machine-learning and statistical modeling tools and techniques (e.g. random forests, gradient-boosted regression, LASSO, logistic regression)
• Experience applying theoretical models in an applied environment

Nice To Haves

• Experience converting research studies into tangible real-world changes
• Experience with discrete and continuous optimization methodologies and algorithms
• Experience applying quantitative analysis to solve business problems and making data-driven business decisions

Responsibilities

• Collaborate with operational science teams to integrate community risk signals into existing operational models and decision-making systems, with a focus on quantifying performance lift and defining integration architecture
• Design and execute experiments to measure how community-impacting operational policies affect business outcomes
• Build automated causal discovery systems leveraging knowledge graphs, LLMs, and document understanding to uncover relationships between operational policies and community outcomes
• Design and deploy production ML forecasting systems with extended prediction horizons using multi-modal data sources, including survey-based indices, geospatial risk features, and operational metrics
• Mentor junior scientists and contribute to building a research culture that balances high-risk, high-reward innovation with reliable product delivery

Benefits

• 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
• parental leave
• sign-on payments
• restricted stock units (RSUs)

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Ph.D. or professional degree