Data Scientist II: ML & Optimization for NASC/TOM Planning

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

Benefits offered by this job

Health insurance

RSUs

401(k) matching

Paid time off

Parental leave

Job summary

Amazon Science is seeking a Data Scientist II to own the end-to-end ML/optimization cycle for NASC & TOM planning. You will design, build, and ship models that inform middle miles decisions, partnering with engineering and product teams to translate operational needs into measurable outcomes.

The role involves forecasting, network optimization, and GenAI initiatives, with strong emphasis on deployment, monitoring, and stakeholder communication to executive leadership.

Qualifications

• 2+ years of data scientist experience.
• 3+ years experience with SQL/Python or equivalent.
• 3+ years ML/statistical modeling experience.
• Experience applying theoretical models in production environments.
• Experience communicating technical concepts to non-technical audiences.

Responsibilities

• Design and implement ML and optimization solutions for forecasting and network problems.
• Drive end-to-end delivery from data exploration through training, evaluation, deployment and monitoring.
• Develop modeling patterns for forecasting and optimization with advanced techniques.
• Build robust validation, backtesting and leakage/bias detection pipelines.
• Collaborate with Data Eng and Software teams to productionize models and define interfaces.

Skills

SQL

Python

Statistical modeling

Time-series forecasting

ML algorithms

Communication

Education

Master's degree in STEM

Tools

AWS Suite (S3, Redshift, Sagemaker, EMR, Kinesis, Lambda, EC2)

Job description

Amazon Science is seeking a Data Scientist II to own the end-to-end ML/optimization cycle for NASC & TOM planning. You will design, build, and ship models that inform middle miles decisions, partnering with engineering and product teams to translate operational needs into measurable outcomes.

The role involves forecasting, network optimization, and GenAI initiatives, with strong emphasis on deployment, monitoring, and stakeholder communication to executive leadership.