Additional Information
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.
Key job 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.
A day in the life
New data has just landed and promoted to our datalake. You load the data and verify it's overall integrity by visualizing variation across target subsets. You realize we may have made progress toward our goals and begin to test the validity of your nominal results. At midday you grab lunch with new coworkers and learn about their fields or weird interests (there are many). You generate visualizations for the entire dataset and perform significance tests that reinforce specific findings. You meet with peers in the afternoon to discuss your findings and breakdown the remaining tasks to finalize your group report!
Amazon.com · Seattle, WA