Sr Business Research Scientist, RBS

Were 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. Were 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 were driving Amazons next phase of efficiency gains through intelligent automation. As part of RBSs Growth organization we operate at the intersection of technical innovation and business impact.

- 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
- Bachelors degree or equivalent
- 3 years leading complex technical projects

- Masters 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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