Data Scientist - NA Operations, AI & Forecasting

Amazon Inc.; Seattle, Northern (WA, KY); Hybrid; USD 108,000 - 160,000; Full time

Job summary

Amazon.com Services LLC in Bellevue, WA is seeking a Data Scientist to drive development of ML, optimization, and causal models for warehouse operations. You will partner with stakeholders, own model development, and leverage AI to deliver actionable insights for Operations and Finance.

The role emphasizes end-to-end project ownership, data enablement, and clear communication of complex results to technical and non-technical audiences, with a focus on scalable data-driven decisions.

Qualifications

• 1+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
• 2+ years of data/research scientist, statistician or quantitative analyst in an internet-based company with complex and big data sources experience
• Master's degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in Science, Technology, Engineering, or Mathematics (STEM)

Responsibilities

• Interfacing with stakeholders to understand business problems and translating these into high and low-level design documents.
• Building machine learning, optimization, and causal inference models that target business-specific challenges.
• Sourcing, cleaning, and analyzing large and complex data sets associated with warehouse operations to provide business-critical insights at scale.
• Leveraging AI-based solutions to drive development and provide nuanced insights into model outcomes.

Skills

SQL

Python

Statistical software

Education

Master's degree in STEM

Tools

R

SAS

Matlab

Job description

Amazon.com Services LLC in Bellevue, WA is seeking a Data Scientist to drive development of ML, optimization, and causal models for warehouse operations. You will partner with stakeholders, own model development, and leverage AI to deliver actionable insights for Operations and Finance.

The role emphasizes end-to-end project ownership, data enablement, and clear communication of complex results to technical and non-technical audiences, with a focus on scalable data-driven decisions.