Drimva

Amazon

Manager III, Data Science - AMZ14164.12

Herndon, VA · Manager · Full-time · Posted Oct 22, 2026

Experience
5 years with a Master’s degree, or 7 years of progressively responsible experience with a Bachelor’s degree; additional specified experience: 5 years in large-scale statistical and machine learning models, 2 years leading teams, 5 years building automated analytical or ML pipelines, 5 years in SQL, and 5 years in Python.
Education
Master’s degree or foreign equivalent in Computer Science, Mathematics, Statistics, Machine Learning, or a related quantitative discipline; alternatively, a Bachelor’s degree or foreign equivalent in one of these fields.

About the job

Leads a data science team building and deploying machine learning systems for infrastructure capacity planning, demand forecasting, and resource optimization.

What you'll do

  • Develop predictive models, statistical frameworks, and risk-based planning approaches for infrastructure decisions.
  • Build and maintain production ML pipelines, experimentation frameworks, and model validation and monitoring.
  • Manage and mentor data scientists and engineers; collaborate with partner teams and present model performance findings to senior leadership.

What they ask for

  • Master’s degree or foreign equivalent in a stated quantitative discipline plus 5 years of relevant experience, or a Bachelor’s degree or foreign equivalent in a stated quantitative discipline plus 7 years of progressively responsible relevant experience.
  • 5 years applying statistical and machine learning models to large-scale systems.
  • 2 years leading teams in a data science or machine learning environment; 5 years building automated analytical or ML pipeline systems.
  • 5 years of experience in SQL and 5 years of experience in Python.

Pay

$175100 - $236900 (yr) — base pay

Tools

Python, SQL

AI focus

Leads development and deployment of machine learning models for capacity planning, demand forecasting, resource optimization, and risk-based planning; oversees production ML pipelines, experimentation, validation, and monitoring.

Model-written summary · the original posting is authoritative

First seen Oct 28, 2026 · Last verified Nov 5, 2026 (UTC)