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Program Manager, Predictions & Machine Learning , NASC Flow, Labor And Tech

Job in Bellevue, King County, Washington, 98009, USA
Listing for: Amazon
Full Time position
Listed on 2026-10-31
Job specializations:
  • IT/Tech
    AI Business & Operations, Data Scientist, Data Analyst, Change Management
Job Description & How to Apply Below
Role Overview The Predictions & Machine Learning charter within the Flow, Labor & Tech (FLT) team of NA Sort Centers is looking for a Program Manager to launch and drive programs related to predictive modeling and machine learning for labor demand forecasting. Come be part of the team that enables the NASC middle mile network to process eight billion+ packages for our customers.

The Predictions charter owns two flagship products — ARES and AARP — that power demand forecasting and labor capacity predictions across the sort center network. As a Program Manager on this charter, you will have the opportunity to work closely with experienced Product Managers, Data Scientists, and ML Engineers to drive improvements in prediction accuracy, model adoption, and operational decision-making ition Scope & Impact As a member of the FLT Products team, you will partner with multiple stakeholders — both within NASC (Operations, PXT, Planning teams) and across associated Tech teams — to create new programs that improve the accuracy and reliability of labor demand predictions.

You will collaborate with Data Scientists and ML Engineers to translate model outputs into actionable insights for operations leaders.

The main focus of this role is driving programs and efforts in the Predictions charter for the middle mile network. You will lead programs to drive Ops knowledge and adoption of prediction tools, provide reporting on model performance and accuracy improvements, and contribute to greenfield development of ML-powered prediction capabilities.

Key job responsibilities
1.
Focus on improving accuracy and reliability of prediction models (ARES, AARP), enhance model performance monitoring, and eliminate forecasting errors in the labor planning process.
2.
Drive greenfield development of ML-powered prediction programs and ensure all product feature requests are routed to the appropriate Product Manager.
3.
Support the training and launch of new prediction tools and model features, including troubleshooting of model-related errors and data anomalies.
4.
Initiate and drive programs to improve Ops knowledge and adoption of prediction products (ARES, AARP) across the NASC network.
5.
Partner with Data Scientists, ML Engineers, and Ops users to research business pain points and develop use cases for improvements in prediction accuracy and model coverage.
6.
Determine success metrics, build model performance dashboards, and audit prediction accuracy on a weekly basis for mature and emerging ML work streams.
7.
Develop documentation and present model performance insights, program roadmaps, and strategic recommendations to L8-level stakeholders on a regular basis.
8.
Determine initiatives that will standardize, centralize, and automate prediction pipelines and model deployment processes across the network.
9.
Deep dive and root-cause prediction misses using data, create metrics-driven, actionable, and sustainable solutions to improve forecast reliability.

Basic qualifications- 3+ years of program or project management experience- 3+ years of defining and implementing process improvement initiatives using data and metrics experience
- Knowledge of Excel (Pivot Tables, VLookUps) at an advanced level and SQL
- Experience using data and metrics to determine and drive improvements
- Experience working cross functionally with tech and non-tech teams
- Bachelor's degree
- Experience using AI-assisted development tools in academic or professional settings

Preferred qualification - 3+ years of driving end to end delivery, and communicating results to senior leadership experience- 3+ years of driving process improvements experience
- Experience in stakeholder management, dealing with multiple stakeholders at varied levels of the organization
-…
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