Amazon
Senior Manager, Science and BI Lead, WWOS Tech
$219K–$296K
Seattle, WA · Manager · Full-time · Posted Oct 16, 2026
- Experience
- 10+ years required; preferred experience includes 10+ years applying ML to complex, large-scale problems, 5+ years hands-on in big data, ML, and predictive modeling, and 5+ years of people management.
- Education
- Master’s degree in Computer Science (Machine Learning, AI, Statistics, or equivalent); PhD in a stated field preferred.
About the job
Leads WWOS Tech’s Science and BI organization, setting its AI/ML strategy and delivering production models and responsible-AI governance for security technology products.
What you'll do
- Own the enterprise AI/ML roadmap and deliver production models, shared experimentation and MLOps capabilities, and model delivery standards.
- Lead and grow Science and BI teams, including managing BIE managers and advancing organization-wide AI literacy.
- Partner with business, technical, legal, privacy, and security leaders on AI solutions, governance, and risk assessments.
What they ask for
- 10+ years of experience building large-scale machine learning and AI solutions at Internet scale.
- Master’s degree in Computer Science (Machine Learning, AI, Statistics, or equivalent).
- Experience translating informal customer requirements into problem definitions while handling ambiguity and competing objectives.
- Experience hiring and leading experienced scientists and developing junior members.
Nice to have
- 10+ years applying ML to complex problems at large scale; 5+ years of hands-on big data, machine learning, and predictive modeling; 5+ years of people management.
- Experience with big data technologies such as AWS, Hadoop, Spark, Pig, or Hive; programming languages such as Java or C++ and scripting languages such as R, MATLAB, or Python; or a PhD in a stated field.
Pay
218,800.00 - 295,900.00 USD annually (base salary)
Tools
SageMaker, AWS, Hadoop, Spark, Pig, Hive, Java, C++
AI focus
Leads enterprise AI/ML strategy and production model delivery, including responsible AI, bias detection, explainability, human-in-the-loop controls, and AI governance.
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