Data Scientist II

Own measurement, modeling, and experimentation for Amazon’s drone-delivery service, turning large-scale data into decision-ready recommendations.

Responsibilities

• Design and deliver data science solutions using machine learning , statistical modeling , and generative AI when the best approach is not immediately obvious
• Acquire, transform, and validate large and evolving operational and customer datasets
• Investigate anomalies and data quality , partnering with data engineers to productionize models and metrics
• Design, run, and analyze A/B tests and quasi-experimental studies to quantify impact and opportunity size
• Perform customer-experience deep dives to identify root causes behind metric movement, connecting outcomes to drivers and translating findings into clear recommendations
• Communicate complex analysis to technical and non-technical audiences, influencing roadmap and prioritization with recommendations
• Own workstream delivery end-to-end, from problem definition through ongoing measurement, in partnership with data engineering, product, and business teams as the business scales

Requirements

• 2+ years in a data scientist (or similar) role with experience in data extraction, analysis, statistical modeling, and communication
• 2+ years experience with data querying languages such as SQL and Hadoop/Hive
• 3+ years in ML/statistical modeling analysis using tools and techniques, including experience with parameters that affect performance
• Master’s degree in a quantitative field, or Bachelor’s plus 5+ years in a quantitative field (e.g., statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science)
• Experience applying theoretical models in an applied environment

Preferred Qualifications

• Experience in Python , Perl , or another scripting language
• Experience in an ML or data scientist role at a large technology company
• Experience developing experimental and analytic plans, using strong baselines, and accurately determining cause-and-effect relationships
• Experience applying causal inference methods, including experimentation/A-B testing and quasi-experimental or observational causal approaches such as DiD , IV , or causal DAGs
• Experience designing, building, or reasoning over knowledge graphs (entity/ontology modeling, graph databases, or graph embeddings)

Technologies

• SQL , Hadoop/Hive , Python , Perl
• Machine learning , statistical modeling , generative AI
• A/B testing , quasi-experimental studies
• Causal inference methods : DiD , IV , causal DAGs

Benefits

• Sign-on payments
• Restricted stock units (RSU)
• Health insurance (medical, dental, vision, prescription) and Basic Life & AD&D insurance, with option for Supplemental life plans
• EAP and Mental Health Support
• Medical Advice Line
• Flexible Spending Accounts
• Adoption and Surrogacy Reimbursement coverage
• 401(k) matching
• Paid time off
• Parental leave

A Day in the Life

• Review model performance metrics before working sessions with engineers to refine a data pipeline
• Prototype a new machine learning approach, run experiments, and compare results against baseline models
• Present preliminary findings to business partners and translate statistical outputs into plain-language recommendations
• Join team discussions, scientific reviews, and mentoring conversations

Location: Seattle, WA (onsite)

Salary: USD 136,000 - 184,000 per yearly

Minimum Experience: 2 years