Job Description
DESCRIPTION
As a Data Scientist you will be 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.
Our team rewards curiosity while maintaining a laser-focus in bringing products to market. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the forefront of both academic and applied research in this product area, you have the opportunity to work together with a diverse and talented team of scientists, engineers, and product managers and collaborate with other teams.
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.
- Design and analyze experiments and observational studies with rigorous statistical inference, including confidence intervals, power/sample-size estimation, variance reduction, and appropriate hypothesis testing.
- Benchmark models and datasets using classical and modern techniques; select ML methods based on data and operational constraints, and evaluate using robust metrics and diagnostic analyses.
- Apply production-grade measurement and MLOps practices, including data quality monitoring, drift/shift detection, and A/B test design and readouts with disciplined diagnosis of metric movement.
- 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!
About the team
Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the limits. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you.
BASIC QUALIFICATIONS
- Master's degree in computer science, machine learning, engineering, or related fields, or Bachelor's degree and 3+ years of data scientist experience
- Experience delivering end-to-end analyses: data cleaning/QC, modeling, evaluation, and stakeholder-ready reporting.
- Experience with statistical foundation for experiments and inference: confidence intervals, power/sample size, two-sample and permutation tests, sequential testing, and multiple-comparison control.
- Experience with model evaluation and diagnostics, including ranking/discrimination metrics (e.g., AUROC and precision-recall-gain), probabilistic metrics (e.g., proper scoring rules and divergence-based measures), and calibration assessment (e.g., reliability diagrams, ECE).
- Proficient in SQL and Python; able to write production-quality analysis code (testing, version control, reproducible workflows).
- Experience working with large-scale data pipelines and production analytics environments (data warehouses/lakes, workflow orchestration, batch or streaming processing).
Job Tags
Full time, Flexible hours, Shift work,