Senior Manager, Science and BI Lead, WWOS Tech
Listed on 2026-10-18
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IT/Tech
AI Engineer (Applied/Software), Machine Learning/ ML Engineer, AI Business & Operations, Data Scientist
Job : | Services LLC
Have you ever wanted to solve a mystery or be part of solving a case? Are you fascinated by detective stories or crime shows on TV? Do you love to manage huge volumes of data, have a science background and enjoy solving complex problems?
WWOS Tech is transforming into an AI-first security technology organization, and we're seeking an exceptional Applied Science Manager to anchor this transformation. As the Science & BI Lead, you'll own the enterprise AI/ML roadmap, lead an organization of scientists and BIEs, and deliver production AI and ML models that will generate 1M+ efficiency hours annually.
Key Job Responsibilities Lead AI/ML Strategy & Delivery- Own the enterprise AI/ML roadmap across UNITE (theft detection, investigation automation), PRISM (operational disruption, risk management) and other WWOS Tech products
- Deliver net-new production AI models by EOY 2026 aligned to WWOS SPS goals: reduce theft/fraud loss from 0.30% to 0.19% of GMS and transform incident preparedness from reactive to proactive.
- Establish AI/ML delivery standards: model quality gates, bias detection, responsible AI compliance (Amazon Trust principles, EU AI Act), and production readiness criteria
- Build centralized model registry, shared experimentation platform (Sage Maker), and MLOps infrastructure in partnership with Data Engineering
- Lead Science & BI pillar within WWOS Tech: grow Science team over next 18 months, manage 3 BIE managers overseeing BIEs across EESN, Ops Disruption, and Business Reporting teams
- Recruit, onboard, and retain top AI/ML talent in a highly competitive market; develop career paths for Scientists, ML Solutions Architects, and BIEs transitioning to AI-enabled strategic advisors
- Drive AI literacy across all of WWOS organization: 100% AI-trained by EOY 2026 across Technical, Leadership, and Cross-Skill tracks
- Establish operating rhythm: weekly Science pillar sync, bi-weekly cross-pillar integration reviews, monthly AI portfolio health inspections
- Translate ambiguous business problems into AI/ML solutions through direct partnership with field leaders, program vertical leaders, and WWOS senior leadership.
- Represent WWOS Tech in Amazon-wide AI/ML forums: AWS AI partnerships, responsible AI governance, GOS AI forums
- Ensure all AI models touching sensitive security data meet Amazon's responsible AI bar and evolving regulatory requirements (GDPR, EU AI Act)
- Implement bias detection, model explainability and human-in-the-loop mechanisms for high-risk applications
- Conduct quarterly AI risk assessments with Legal, Info Sec, and Privacy teams; maintain AI model inventory and compliance dashboard
- Partner with AI Ethics & Governance Specialist to establish enterprise-wide responsible AI frameworks
We are a team that cares about your work-life balance, while challenging you to solve problems at high scale. You will be part of a strong team in a fast-paced, start-up environment where agile development is embraced and innovation is encouraged. You will get support and resources from some of the smartest people in the industry to continue your personal and professional growth.
You'll be joining a fun team that prides itself on a great work environment with an inclusive group of people that loves working together towards a common goal and make history in launching a new strategic service in the industry.
- 10+ years of building large-scale machine learning and AI solutions at Internet scale experience
- Master's degree in Computer Science (Machine Learning, AI, Statistics, or equivalent)
- Experience building large-scale machine learning and AI solutions at Internet scale
- Experience distilling informal customer requirements into problem definitions, dealing with ambiguity and competing objectives
- Experience hiring and leading experienced scientists as well as having a successful record of developing junior members from academia or industry to a successful career track
- 10+ years of practical work applying ML to solve complex problems for large-scale applications experience
- 5+ years of hands‑on work in big data, machine learning and predictive modeling experience
- 5+ years of people management experience
- PhD in Computer Science (Machine Learning, AI, Statistics, or equivalent)
- Experience in practical work applying ML to solve complex…
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