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🚀 My Contributions

· 41 min read

I wanted to document my major professional contrributions; thus I made this post.

A focused timeline of my key projects, showcasing specific deliverables and measurable impact from newest to oldest.

Interactive Timeline: Click on the short IDs (like [P1], [R1], etc.) in the timeline above to jump to detailed project information below.

Project Details​

2025-09: Driver Feedback Agentic AI System​

Amazon needed to improve customer service efficiency for driver-related concerns, which were generating high contact volumes and customer frustration.

2025-06: GenAI Tools Adoption​

Amazon needed to accelerate GenAI adoption across the organization to improve developer productivity and innovation.

2025-03: Raising the Experimentation Bar Across Amazon's Customer Service Organization​

Amazon Customer Service needed to establish comprehensive experimentation practices and weblab guidance across teams to improve data-driven decision making and innovation.

2025-01: Team Ideation & Innovation Leadership​

Amazon needed to establish team motivation mechanisms and innovation processes to drive ideation and increase the likelihood of leadership adoption for experimental ideas.

2024-08: F2 Search Customer Experience Rapid Experimentation with Mechanized Collection Discovery Widgets​

Amazon Fashion Technology needed a comprehensive experimentation framework for Search & Collections (SCX) to test and optimize customer experience across fashion search and merchandising.

2024-08: Fashion and Fitness Customer Search Journey Analysis Framework​

Amazon Fashion & Fitness needed comprehensive customer journey analysis to understand and optimize the customer experience across fashion and fitness product categories.

2024-08: Org Wide Success Metric Framework​

Amazon needed a standardized organization-wide success framework to measure and record success across different teams and initiatives, requiring L8+ leadership buy-in.

2024-06: Experiment Bar Raiser & Standards​

Amazon needed to establish higher standards for experimentation across the organization and ensure consistent quality in weblab design and analysis.

2023-09: Designed and Architected the Cicada Search and Discovery Experience​

Amazon needed to establish comprehensive search and discovery capabilities for Cicada items, requiring deep technical understanding of Amazon's search stack, custom recommendation widgets, search index integration, and a northstar vision for the platform.

2023-06: Cicada Digital Fulfillment Service​

Amazon needed a central, mission-critical Tier-1 Cicada Digital Fulfillment Service to handle high-volume customer transactions with reliability and scalability for the Cicada platform.

2020-06: Radian ML Pipeline Optimization​

Radian (PMI mortgage insurance company) needed a high-scale image processing system to handle 1.5+ billion images with 1.9 million monthly increases, requiring 700+ images/second processing capability. The existing GPU-intensive ML solution had performance bottlenecks causing a 2-billion-image backlog, threatening contract renewal.

2020-05: Farmers Insurance ML Integration & Solution Delivery​

Farmers Insurance needed ML models for document similarity analysis and roof hazard classification to be properly integrated into a holistic solution that would automate insurance processing and risk assessment.

2020-03: TrustStar Platform Architecture​

TrustStar needed a multi-tenant financial insights platform supporting both institutional and individual users with KYC integration and delegated authentication.

2020-01: United Healthcare Social Media Lead Generation​

United Healthcare needed a social media listening system to identify and generate leads from healthcare conversations across social platforms, requiring A/B testing framework for multi-channel lead generation campaigns.

2019-11: Barclays Wealth Management CEO Demos & Workshops​

Barclays Wealth Management needed to understand and adopt CognitiveScale's Profile-of-One platform capabilities, requiring executive-level demonstrations and comprehensive workshops to ensure successful platform adoption and organizational buy-in.

2019-07: Profile-of-One Platform​

CognitiveScale needed a core platform feature that would serve as the company's primary market differentiator to drive customer adoption and competitive advantage.

Contribution Buckets​

AI & Machine Learning Contributions​

  • GenAI Adoption: Pioneered GenAI tools across Amazon, established MCP server with 25+ prompts
  • AI Systems: Designed Driver Feedback AI system, achieved 75% contact reduction
  • Radian ML Pipeline: Re-architected 1.5B+ image processing system, achieved 536% throughput increase + 700+ images/second + 2B image backlog cleared
  • Insurance ML: Developed Farmers Insurance ML models, achieved 0.8 recall at k6
  • Applied AI: Delivered 5+ complex AI solutions, earned Customer Hero Award

Technical Leadership Contributions​

  • System Architecture: Designed Tier-1 Cicada Digital Fulfillment Service handling 250+ TPS
  • Team Management: Led 14+ engineer teams, aligned 75+ stakeholders
  • Platform Development: Invented Profile-of-One platform, company's primary differentiator
  • Process Innovation: Streamlined AI Development Life Cycle, reduced architecture time by 15%

Experimentation & Process Contributions​

  • SDS Launch: Led complete Self-Service Driver Support system launch with 12+ team coordination
  • F2-SCX Framework: Designed and implemented Fashion Tech Search & Collections experimentation system
  • Customer Journey Analysis: Comprehensive customer journey mapping and optimization for Fashion & Fitness
  • Weblab Guidance: Established weblab best practices framework adopted across organization
  • MacAds Integration: Led Machine Learning Ads integration workstream for context-aware advertising
  • Experimentation: Developed weblab dial-up strategies and exposure control adoption
  • Process Innovation: Created comprehensive runbooks and experimentation guidance
  • Success Framework: Organization-wide success measurement and recording framework with L8+ buy-in

Experimentation Standards Contributions​

  • Experiment Bar Raiser: Achieved WLBR graduation with comprehensive experimentation standards
  • Weblab Standards: Established weblab best practices framework adopted across organization
  • Quality Frameworks: Created sustainable experimentation processes and quality standards
  • Training Materials: Delivered comprehensive WLBR training and grading materials
  • Analysis Systems: Implemented experimentation analysis and metrics tracking systems

Leadership & Team Development Contributions​

  • Team Ideation: Incubated and influenced 50+ experiment ideas across 7 hackathon sessions
  • Innovation Leadership: Led Innovation Friday coordination and hackathon leadership
  • Technical Mentorship: Developed interview calibration and hiring processes for team development
  • Process Establishment: Created sustainable team collaboration and office hour systems
  • Leadership Adoption: Increased likelihood of leadership adoption through practical POC development

Business Impact Contributions​

  • Customer Service: Reduced 1M+ annual customer contacts through AI automation
  • Revenue Generation: Secured contract renewals and 4 expansion deals
  • Cost Optimization: Achieved $2.5M annual operational cost savings
  • Market Differentiation: Created platform features driving significant customer adoption

CognitiveScale Platform Contributions​

  • Profile-of-One Platform: Invented company's primary market differentiator with 5+ Fortune 500 solutions
  • Radian Image Processing: Built 1.5B+ image processing system with 700+ images/second throughput
  • Farmers Insurance ML: Developed document similarity and roof hazard classification models
  • TrustStar Architecture: Designed multi-tenant financial insights platform with KYC integration
  • United Healthcare Social Media: Built social media listening system with 6M+ healthcare prospects and A/B testing
  • ML Pipeline Optimization: Achieved 536% throughput increase, cleared 2B image backlog

Key Verbs in My Contributions​

AI & Technical Verbs​

  • Pioneered: GenAI adoption across Amazon, MCP server establishment
  • Designed: Driver Feedback AI system, Tier-1 Cicada Digital Fulfillment Service
  • Re-architected: ML pipeline achieving 536% throughput increase
  • Invented: Profile-of-One platform, company's primary differentiator

Leadership Verbs​

  • Led: 14+ engineer teams, 10+ engineer teams for critical projects
  • Aligned: 75+ stakeholders, 12+ teams including Applied Scientists
  • Managed: Complex stakeholder relationships, technical dependencies
  • Drove: Seamless integration, organizational GenAI adoption

Process & Experimentation Verbs​

  • Established: Weblab best practices framework, SDS launch processes, F2-SCX experimentation system
  • Coordinated: 12+ teams for SDS launch, weblab guidance adoption, MacAds integration
  • Developed: Weblab dial-up strategies, experimentation frameworks, LLM-based search widgets
  • Created: Comprehensive runbooks, experimentation guidance materials, metrics infrastructure

Experimentation Standards Verbs​

  • Pursued: WLBR graduation with comprehensive training and grading rubric development
  • Developed: Comprehensive grading rubric for WLBR with examples and best practices
  • Established: Experimentation standards and frameworks for consistent quality
  • Implemented: Weblab analysis and metrics tracking systems

Leadership & Team Development Verbs​

  • Incubated: Ideas across the team through structured ideation sessions
  • Led: 7 hackathon sessions generating 50+ experiment ideas, innovation Friday coordination
  • Ensured: POCs reflected practical and clear value for leadership adoption
  • Established: Team processes, office hours, brainstorming frameworks, technical mentorship
  • Implemented: Interview calibration, hiring processes, team collaboration systems

Impact Verbs​

  • Achieved: 75% contact reduction, 1M+ annual contact reduction
  • Secured: Contract renewals, 4 expansion deals, Customer Hero Award
  • Delivered: 25+ productivity prompts, mission-critical systems
  • Generated: 10+ commercial opportunities, significant customer adoption

Glossary​

Amazon Acronyms​

  • APT: Amazon Personalization Technology - Amazon's personalization and recommendation platform
  • F2-SCX: Fashion & Fitness Search & Collections - Amazon's fashion and fitness search and merchandising system
  • L8+: Level 8 and above - Senior leadership levels at Amazon
  • MacAds: Machine Learning Ads - Amazon's machine learning advertising platform
  • SDS: Self-Service Driver Support - Amazon's self-service system for driver support
  • TPS: Transactions Per Second - A measure of system throughput and performance
  • Weblab: Amazon's experimentation platform for A/B testing and feature experimentation
  • WLBR: Weblab Bar Raiser - Amazon's certification program for experimentation excellence

Technical Acronyms​

  • AI: Artificial Intelligence
  • API: Application Programming Interface
  • CLI: Command Line Interface
  • GPU: Graphics Processing Unit
  • LLM: Large Language Model
  • ML: Machine Learning
  • MCP: Model Context Protocol - Protocol for AI model integration
  • POC: Proof of Concept
  • SLA: Service Level Agreement

Business Acronyms​

  • DDA: Development Discussion and Action - Amazon's performance review process
  • Fortune 500: List of the 500 largest companies in the United States
  • P&L: Profit and Loss - Financial statement showing revenues and expenses
  • ROI: Return on Investment
  • SLA: Service Level Agreement

Stakeholder Collaboration​

🏢 Internal Amazon Teams​

Leadership & Strategic Teams​

  • L8+ Leadership: Organization-wide success framework buy-in and strategic decision support
  • Skip-Level Leadership: Success framework pitching and L8+ buy-in for organization-wide adoption
  • Executive Sponsors: Strategic alignment and value delivery across multiple initiatives

Engineering & Technical Teams​

  • Driver Support Engineering: Driver Feedback AI system implementation and contact reduction
  • SDS Engineering Teams: Self-Service Driver Support system launch and weblab guidance adoption
  • F2-SCX Engineering: Fashion Tech Search & Collections experimentation framework development
  • MacAds Integration Teams: Machine Learning Ads integration for context-aware advertising
  • GenAI Engineering Teams: MCP server adoption and productivity prompt development
  • Customer Service Engineering: AI automation and contact reduction optimization
  • Data Platform Teams: Metrics infrastructure and experiment analysis systems

Product & Business Teams​

  • Fashion Technology Product: Customer journey analysis and search optimization
  • Customer Service Product: Driver feedback system and contact reduction strategies
  • Experimentation Product: Weblab guidance and experimentation framework adoption
  • GenAI Product Teams: MCP server integration and productivity tool adoption

Data & Analytics Teams​

  • F2-SCX Data Teams: Fashion tech experimentation metrics and analysis
  • Customer Service Analytics: Contact reduction metrics and impact measurement
  • Experimentation Analytics: Weblab analysis and metrics tracking systems
  • GenAI Analytics: Usage tracking and adoption metrics across Amazon

Process & Standards Teams​

  • Weblab Bar Raiser Office: WLBR training, grading rubric development, and standards establishment
  • Experimentation Standards: Quality frameworks and best practices adoption
  • Team Development Teams: Innovation processes, ideation sessions, and technical mentorship
  • Hiring & Interview Teams: Interview calibration and technical hiring processes

Cross-Functional Stakeholders​

  • 75+ Technical Stakeholders: Digital Fulfillment Service alignment and architecture decisions
  • 12+ Engineering Teams: SDS launch coordination and weblab guidance adoption
  • Marketing Teams: Social media strategy and campaign performance optimization
  • Operations Teams: System reliability, performance monitoring, and oncall processes
  • Training Teams: WLBR candidate progress tracking and documentation systems

🏥 Healthcare Industry Partners​

  • United Healthcare: Social media listening system and 6M+ healthcare prospect identification
  • Health Markets & UHOne: A/B testing framework and multi-channel lead generation campaigns
  • Farmers Insurance: ML model development and document processing automation
  • Insurance Agents: Roof hazard classification and risk assessment optimization

🏦 Financial Services Partners​

  • TrustStar: Multi-tenant platform architecture and KYC integration
  • Financial Institutions: User onboarding workflows and delegated authentication
  • Mortgage Insurance (Radian): 1.5B+ image processing and room condition assessment

🏭 Enterprise Clients​

  • Fortune 500 Companies: Profile-of-One platform adoption and 5+ major solutions
  • CognitiveScale Customers: ML pipeline optimization and 536% throughput improvement
  • Contract Renewals: 4 expansion deals and $2.5M annual cost savings

👥 Cross-Functional Teams​

  • Data Scientists: ML model development and experimentation frameworks
  • Marketing Teams: Social media strategy and campaign performance optimization
  • Sales Teams: Lead generation and customer acquisition optimization
  • Product Teams: Feature development and user experience enhancement
  • Operations Teams: System reliability and performance monitoring

🌐 External Partners​

  • Brandwatch: Social media listening API integration and healthcare conversation monitoring
  • Neustar: Database integration and prospect matching for lead scoring
  • Cloud Providers: Azure blob storage, AWS infrastructure, and Snowflake integration
  • Social Platforms: Twitter, Facebook, LinkedIn, YouTube, Instagram API integrations

Impact Summary​

📊 Scale & Reach​

  • 1.5B+ Images Processed: Radian mortgage insurance image processing platform
  • 6M+ Healthcare Prospects: United Healthcare social media lead generation system
  • 1M+ Annual Contact Reduction: Driver Feedback AI system automation
  • 75+ Stakeholder Alignment: Cicada Digital Fulfillment Service coordination
  • 50+ Experiment Ideas: Team ideation sessions across 7 hackathon events
  • 25+ Productivity Prompts: GenAI tools adoption across Amazon

💰 Business Impact​

  • $2.5M Annual Cost Savings: ML pipeline optimization and efficiency improvements
  • 4 Expansion Deals: Contract renewals and business growth
  • 5+ Fortune 500 Solutions: Profile-of-One platform enterprise adoption
  • 536% Throughput Increase: ML pipeline performance optimization
  • 75% Contact Reduction: Customer service automation impact

🏗️ Technical Leadership​

  • Mission-Critical Systems: Tier-1 Cicada Digital Fulfillment Service (250+ TPS)
  • AI/ML Innovation: GenAI adoption, experimentation frameworks, and ML optimization
  • Platform Architecture: Multi-tenant systems, social media intelligence, and image processing
  • Team Development: WLBR graduation, technical mentorship, and innovation processes
  • Cross-Industry Expertise: Healthcare, financial services, insurance, and e-commerce

🌍 Industry Influence​

  • Healthcare: Social media listening, lead generation, and insurance ML models
  • Financial Services: Multi-tenant platforms, KYC integration, and user onboarding
  • E-commerce: Fashion tech experimentation, customer journey analysis, and search optimization
  • Enterprise Software: Profile-of-One platform, ML pipeline optimization, and AI automation

Looking Forward​

This timeline demonstrates my consistent approach to professional challenges: navigating ambiguity with systematic thinking, making thoughtful technical decisions, and creating meaningful impact through collaboration and continuous learning.

Each contribution builds on previous learnings, creating a foundation of experience that enables me to tackle increasingly complex challenges and create greater impact.

The common thread across all my contributions is a focus on solving real problems, making thoughtful decisions, and creating value for users, teams, and organizations.

🤖 AI Metadata (Click to expand)
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# - Cross-team Influence: Impacting multiple teams and organizations through technical leadership
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# - Team Building: Team motivation, ideation sessions, and collaboration frameworks
# - Technical Standards: Experiment bar raiser role and technical excellence initiatives
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# - Customer Journey Analysis: Fashion & Fitness customer experience analysis and optimization
# - Success Framework: Organization-wide success measurement and recording framework
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# RECENT_ITERATION_CHANGES:
# - Cicada Digital Fulfillment Service: Renamed from "Digital Fulfillment Service" to emphasize Cicada platform focus
# - Cicada Search & Discovery: Added new project for search and discovery contributions (2023-09)
# - Chronological Ordering: Fixed timeline to show Search & Discovery (2023-09) before Digital Fulfillment Service (2023-06)
# - Project Grounding: Updated grounding to reference specific Cicada files instead of generic QC files
# - File References: Added specific note files for Search & Discovery work:
#   - 🏢250315🦗 Cicada Search Discovery Northstar.txt (northstar vision)
#   - 🏢250320🦗 Recommendation Widgets Architecture.md (custom widgets design)
#   - 🏢250325🦗 Search Indexing Integration Meeting.txt (search integration)
#   - 🏢250410🦗 Search Discovery Implementation Notes.md (implementation details)
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# - Project Title Refinements: Updated project titles for accuracy and specificity:
#   - F2-SCX Experimentation Framework → F2 Search Customer Experience Rapid Experimentation with Mechanized Collection Discovery Widgets
#   - Customer Journey Analysis Framework → Fashion and Fitness Customer Search Journey Analysis Framework
#   - Raising the Experimentation Bar in Amazon Customer Service → Raising the Experimentation Bar Across Amazon's Customer Service Organization
# - Content Accuracy Updates: Rephrased Farmers Insurance project to reflect performance measurement and contract compliance role
# - Detailed Impact Metrics: Added specific metrics to experimentation bar project (50+ experiment designs reviewed, 7 major concerns identified)
# - Best Practice Dashboard: Documented development of best practice adoption dashboard for qualitative experiments
#
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# 5. CONTENT_GROUNDING:
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#
# 6. QUALITY_VERIFICATION:
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#
# 7. TESTING_CHECKLIST:
#    - Test clicking on new timeline card navigates to correct section
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#
# 8. DOCUMENTATION_UPDATE:
#    - Update AI metadata with new project details
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#
# EXAMPLE_WORKFLOW:
# 1. Identify new project from notes/resume/context
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# 6. Insert in correct chronological position
# 7. Test click navigation and verify ordering
# 8. Update AI metadata with new project information
#
# CRITICAL_AI_AGENT_GUIDANCE:
# - ALWAYS search user's notes (NotePlan, Dropbox) before creating content
# - NEVER create content without grounding in actual files
# - ALWAYS maintain chronological order (newest to oldest in detailed sections)
# - ALWAYS update ContributionTimeline.tsx when adding new projects
# - ALWAYS test click navigation after changes
# - ALWAYS wrap "Grounded in" sections in <details><summary>References</summary>
# - ALWAYS use STAR format (Situation, Task, Action, Result) for project descriptions
# - ALWAYS keep timeline cards concise (max 8-10 words for description)
# - ALWAYS verify section IDs match component mappings
# - ALWAYS update AI metadata when making changes
#
# CONTENT_GROUNDING_REQUIREMENTS:
# - Search /Users/omareid/Library/Containers/co.noteplan.NotePlan3/Data/Library/Application Support/co.noteplan.NotePlan3/Notes/ for Amazon projects
# - Search /Users/omareid/Dropbox/Apps/iA Writer c12e/ for CognitiveScale projects
# - Include specific file paths in References sections
# - Verify content authenticity against actual work documentation
# - Never create fictional or assumed content
#
# QUALITY_ASSURANCE_CHECKLIST:
# - Verify all timeline cards are concise and impactful
# - Ensure all detailed sections use STAR format
# - Confirm all References sections are properly grounded
# - Test all click navigation works correctly
# - Validate chronological ordering is maintained
# - Check all short ID mappings are synchronized
# - Ensure no broken links or missing content
#
# COMMON_PITFALLS_TO_AVOID:
# - Don't create content without grounding in actual files
# - Don't break chronological ordering
# - Don't forget to update ContributionTimeline.tsx
# - Don't create overly verbose timeline cards
# - Don't skip the References section
# - Don't assume dates without verification
# - Don't create fictional project details