Data Scientist, Special Projects

Amazon • Seattle, WA

1d

About The Position

We are a passionate team applying the latest advances in technology to solve
real-world challenges. As a Data Scientist working at the intersection of
machine learning and advanced analytics, you will help develop innovative
products that enhance customer experiences. Our team values intellectual
curiosity while maintaining sharp focus on bringing products to market.
Successful candidates demonstrate responsiveness, adaptability, and thrive in
our open, collaborative, entrepreneurial environment.

Working at the forefront of both academic and applied research, you will join a
diverse team of scientists, engineers, and product managers to solve complex
business and technology problems using scientific approaches. You will
collaborate closely with other teams to implement innovative solutions and drive
improvements.

At Amazon, we cultivate an inclusive culture through our Leadership Principles,
which emphasize seeking diverse perspectives, continuous learning, and building
trust. Our global community includes thirteen employee-led affinity groups with
40,000 members across 190 chapters, showcasing our commitment to embracing
differences and fostering continuous learning through local, regional, and
global programs.

We prioritize work-life balance, recognizing it as fundamental to long-term
happiness and fulfillment. Our team is committed to supporting your career
development through challenging projects, mentorship opportunities, and targeted
training programs that help you reach your full potential.

Requirements

• 2+ years of data scientist experience
• 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
• 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
• 1+ years of guiding and coaching a group of researchers experience
• 1+ years of working with or evaluating AI systems experience
• 1+ years of creating or contributing to mathematical textbooks, research papers, or educational content experience
• Master's degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in Science, Technology, Engineering, or Mathematics (STEM)
• Experience applying theoretical models in an applied environment

Nice To Haves

• Ph.D. in Science, Technology, Engineering, or Mathematics (STEM)
• Knowledge of machine learning concepts and their application to reasoning and problem-solving
• Experience in Python, Perl, or another scripting language
• Experience in a ML or data scientist role with a large technology company
• Experience in defining and creating benchmarks for assessing GenAI model performance
• Experience working on multi-team, cross-disciplinary projects
• Experience applying quantitative analysis to solve business problems and making data-driven business decisions
• Experience effectively communicating complex concepts through written and verbal communication

Responsibilities

• Work hands-on with complex, noisy datasets to derive actionable insights and explain/debug black-box models using interpretability and data-attribution methods (e.g., SHAP/TreeSHAP, Anchors, Integrated Gradients, counterfactuals, nearest-neighbor exemplars, influence/data attribution).
• Design and analyze experiments and observational studies with rigorous statistical inference, including confidence intervals, power/sample-size estimation, variance reduction, and appropriate tests (e.g., two-sample tests, permutation tests, sequential testing, and multiple-comparison control such as FDR).
• Benchmark models and datasets using classical and modern techniques; select ML methods based on data and operational constraints (e.g., clustering/KDE, tree ensembles, CNN/RNN/Transformers, representation learning), and evaluate with robust metrics and diagnostics (e.g., AUROC, AUPRG, proper scoring rules/losses, calibration/ECE, threshold/utility curves, slice-based evaluation, and error analysis).
• Apply production-grade measurement and MLOps practices, including data quality monitoring, drift/shift detection (PSI, KS, MMD/embedding drift), and A/B test design and readouts with disciplined diagnosis of metric movement (e.g., instrumentation changes, seasonality, novelty effects, sample-ratio mismatch, guardrail tradeoffs).
• Deliver end-to-end analyses that improve team execution and decision-making—define goal-driving metrics with stakeholders, build clear reporting (tables, dashboards, and visualizations), and communicate results that translate into concrete actions.
• Investigate anomalies and data integrity issues across diverse data sources using structured root-cause analysis, correlation diagnostics, significance testing, and simulation across high- and low-fidelity datasets.
• Partner closely with cross-functional domain experts to design experiments and interpret results, applying modern statistical methods to evaluate predictive and generative models as well as operational and process performance.
• Develop production-quality analytics and modeling code—write well-tested, maintainable SQL/Python scripts and analysis workflows that can be promoted into production pipelines, and continuously adopt new statistical methods and best practices as the field evolves.

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

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level