Data Scientist II — Sourcing & Inventory Optimization

Amazon; Factoria (WA); On-site; USD 136,000 - 184,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Parental leave

Job summary

Amazon is seeking a Data Scientist II in the SCOT OSS - Sourcing Execution & Performance team. You will lead model development to optimize inbound network parameters, analyze PO lifecycle improvements, and explore Gen AI solutions for vendor coaching and dispute evaluation.

The role involves collaborating across Product Management, Operations, and Tech teams, translating complex business logic into scalable models and communicating insights to both technical and non-technical stakeholders.

Qualifications

• 2+ years in data science or similar role with data extraction and analysis.
• 2+ years SQL or Hadoop/Hive querying experience.
• 3+ years ML/statistical modeling, data analysis and performance tuning.
• Master’s degree in a quantitative field, or 5+ years in a quantitative field with relevant degree.
• Experience applying theoretical models in an applied environment.

Responsibilities

• Collaborate with product managers, science and engineering to design model solutions for Sourcing Execution & Performance systems.
• Use large datasets or experiments to make causal inferences or predictions.
• Work with engineers to automate science analysis processes and build scalable measurement solutions.
• Interpret data, write reports, and make actionable recommendations.
• Drive technical standards and mentoring for the team’s Science solutions.

Skills

Data science

SQL

ML/Statistics

Education

Master's degree in a quantitative field

Bachelor’s degree + 5+ years

Tools

Python

Perl

Job description

Amazon is seeking a Data Scientist II in the SCOT OSS - Sourcing Execution & Performance team. You will lead model development to optimize inbound network parameters, analyze PO lifecycle improvements, and explore Gen AI solutions for vendor coaching and dispute evaluation.

The role involves collaborating across Product Management, Operations, and Tech teams, translating complex business logic into scalable models and communicating insights to both technical and non-technical stakeholders.