We're seeking a senior technical analytics leader to drive the development of an intelligent decision-making platform that will transform how Amazon makes operational decisions. This role will architect analytical frameworks that evolve static rules into dynamic, context-aware decisions at scale. The role requires sophisticated technical judgment to design generalized analytical patterns that work across diverse tenant needs while maintaining precision and performance.
Key job responsibilities Technical Architecture & Development (60%):
Architect analytical frameworks that enable pattern detection and sharing across diverse decision types Design generalized analytical models that maintain tenant-specific context while enabling cross-tenant pattern learning Develop scalable validation frameworks for complex multi-tenant pattern detection processing millions of decisions daily Create analytical approaches for real-time context processing that adapt to changing business conditions Design methods for automated pattern discovery and validation across decision domains Build platform-level analytical capabilities that scale across tenant needs
Technical Objectives:
Define foundational analytical patterns for intelligent decision-making at scale Develop novel approaches for context-aware pattern detection using advanced AI/ML models, that work across tenant boundaries Create analytical frameworks that enable pattern sharing while maintaining tenant isolation Design validation approaches that scale across diverse decision types Establish platform-level analytical standards and governance Manage technical dependencies across multiple engineering and science teams Success Metrics: Pattern detection effectiveness across diverse tenant needs Real-time processing capabilities at scale Cross-tenant pattern sharing effectiveness Platform-level analytical performance Tenant adoption and expansion metrics
Technical Requirements: Machine Learning & AI: Expert knowledge of machine learning models and frameworks Proficiency with Large Language Models Strong background in clustering algorithms Deep expertise in designing generalized ML/LLM frameworks that scale Experience creating novel pattern detection approaches Strong background in multi-tenant analytical systems Proven track record of pioneering new analytical methods
Data Engineering: Experience with large-scale data pipelines Distributed computing systems expertise Data validation frameworks Experience with complex multi-tenant data architectures Expertise in real-time analytical processing at scale Strong background in platform-level data modeling Track record of building generalized analytical frameworks
System Architecture: Modular architecture design Microservices architecture API design and development Performance optimization Scalable system design Leadership & Collaboration: Technical project leadership Cross-team collaboration Architecture governance Technical documentation Mentoring and guidance
A day in the life Lead architectural decisions for ML systems and data pipelines Review and optimize model performance metrics Analyze system health and scalability metrics Develop technical specifications and proof of concepts Debug complex production issues Evaluate and approve architecture changes Lead analytical decisions for ML/LLM systems Review and optimize pattern detection metrics Analyze system performance and scalability Develop analytical specifications Debug complex pattern detection issues Evaluate and approve analytical approaches Design solutions for improving decision accuracy
Leadership & Collaboration: Lead technical design reviews and architecture syncs Mentor team members on ML best practices Collaborate with data scientists on model improvements Align with cross-functional teams on technical dependencies Guide implementation strategies across multiple teams Review and approve technical proposals Provide architectural guidance for scaling solutions Strategic Focus: Drive improvements in model accuracy and system performance Define technical standards and best practices Balance immediate technical needs with long-term architecture Evaluate new ML technologies and approaches Guide innovation in automated trend detection Ensure system reliability and scalability Monitor and improve key performance metrics Regular Interactions: ML Engineers and Data Scientists Product and Platform teams Infrastructure teams Business Intelligence teams Senior technical leadership This role combines hands-on technical expertise in ML/AI with strategic architectural leadership, requiring both deep technical knowledge and strong collaboration skills. The focus is on driving technical excellence while ensuring successful delivery of large-scale ML solutions that impact business metrics.
About the team The DecisionIQ team within RBS is transforming how Amazon makes operational decisions. We're building a platform that evolves static rules into intelligent, context-aware capabilities through advanced pattern detection and ML/LLM approaches. Our multi-disciplinary team combines analytics, science, and engineering expertise to solve complex challenges in real-time decision processing and pattern sharing across diverse tenants. Working closely with multiple business domains across Amazon SDO and AWS organizations, we're driving Amazon's next phase of efficiency gains through intelligent automation. As part of RBS's Growth organization, we operate at the intersection of technical innovation and business impact.
Basic Qualifications
- Experience with AWS technologies
- 8+ years of software development experience
- 6+ years of machine learning, statistical modeling, data mining, and analytics techniques experience
- Experience with large distributed IT systems
- Experience using data and metrics to drive improvements
- Bachelor's degree or equivalent
- 3+ years leading complex technical projects
Preferred Qualifications
- Master's degree in statistics, data science, or an equivalent quantitative field
- Experience in online retail
- Experience designing large-scale systems and applications
- Previous experience with visual similarity algorithms
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