Amazon.com Services LLC

Data Science, Science, no business category

SeniorDataScientist,SpecialProjects

$159–215k Seattle, Washington, United States FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“Senior Data Scientist, Special Projects at Amazon.com Services LLC. Skills: Machine learning, Advanced analytics, Statistical inference, MLOps. Derive actionable insights. Explain black-box models”

What You'll Achieve.

Enhance customer experiences; Drive improvements; Improve team execution; Improve decision-making; Translate results into actions

Industry & Context.

Data Science, Science, no business category
Problems you'll solve

Solve real-world challenges; Solve complex problems; Root-cause analysis

What They're Looking For.

Must Have

5+ years data querying languages, 5+ years scripting languages, 5+ years statistical/mathematical software, Master's degree in STEM, Experience applying statistical models, Experience building automated analytical systems

Nice to Have

PhD in STEM, Knowledge of machine learning approaches, Experience in ML role, Experience in data scientist role, 5+ years Data & AI technologies, Experience working on multi-team projects, Experience working on cross-disciplinary projects, Experience applying quantitative analysis, Experience making data-driven decisions

What You'll Do.

Derive actionable insights

Explain black-box models

Debug black-box models

Design observational studies

Analyze observational studies

Apply MLOps practices

Diagnose metric movement

Deliver end-to-end analyses

Define goal-driving metrics

Build clear reporting

Investigate anomalies

Investigate data integrity issues

Perform root-cause analysis

Perform correlation diagnostics

Perform significance testing

Design experiments with domain experts

Interpret results with domain experts

Evaluate predictive models

Evaluate generative models

Evaluate operational performance

Evaluate process performance

Develop production-quality analytics code

Develop production-quality modeling code

Write well-tested scripts

Write maintainable scripts

Adopt new statistical methods

How You'll Work.

Team & Collaboration

Cross-functional teams; Diverse team of scientists; Diverse team of engineers; Diverse team of product managers; Partner with domain experts

Communication Scope

Communicate results; Communicate complex concepts

Full Job Description

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

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