Master's degree in Science, Technology, Engineering, or Mathematics (STEM)
2+ 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 machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
Experience with AWS services including S3, Redshift, Sagemaker, EMR, Kinesis, Lambda, and EC2
Proficiency in statistical modeling and machine learning - time-series forecasting, regression, tree-based methods, and deep learning.
Demonstrated ability to communicate technical results to non-technical business audiences.
3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
USA, WA, Bellevue - 136,000.00 - 184,000.00 USD annually
Additional Information
The NASC & TOM Science team owns Operations Research, Machine Learning, and AI projects across the North America Sort Center (NASC) and Transportation Operations Management (TOM) planning and operations organizations. We turn complex network, labor, and capacity problems into deployed models that drive multi-million-dollar planning decisions every day.
As a Data Scientist II, you will own the end-to-end Machine learning Operation cycle: Design, build, and ship machine learning and/or optimization models that directly shape Amazon's middle miles planning decisions. You will own end-to-end delivery - from problem framing with business partners, through modeling and validation, to deployment in internal model hosting platform and integration with downstream planning tools.
You will work on problems such as:
- Long- and short-horizon forecasting
- Network and capacity optimization
- GenAI / agentic systems
- Defect prevention and adaptive planning
You will partner closely with Engineering, Product, Engineering, and stakeholders to translate ambiguous operational pain points into measurable model outcomes.
Key job responsibilities
- Design and implement complex ML and optimization solutions (forecasting, MIP/LP, simulation, Deep learning / foundation model);
- Drive end-to-end delivery of scalable models - from data exploration and feature engineering through training, evaluation, deployment, and post-launch monitoring;
- Develop new modeling patterns and analytical frameworks for forecasting (multivariate, hierarchical, causal-DAG, model-chaining) and optimization;
- Build robust model validation, backtesting, and monitoring pipelines; identify and eliminate sources of leakage, bias, and silent failure;
- Define and own model performance metrics (e.g., WAPE) tied to business outcomes;
- Partner with Data Engineering and Software Development to productionize models and define I/O contracts, packaging, and model CI/CD;
- Excellent communication to present findings, tradeoffs, and recommendations clearly to stakeholders and senior leadership.
Amazon.com · Bellevue, WA