What I needed to do at a high level

• Rapidly build deep financial acumen to effectively collaborate with principal product managers and finance leaders
• Pioneer first-ever machine-learning systems for managing Amazon’s massive-scale transportation financial data to ultimately shorten the manual efforts of Month End Close (MEC)
• Design solutions to handle transaction volumes exceeding traditional financial tools’ capabilities
• Create new frameworks for MEC processes where no precedent existed
• Build scalable solutions from ground zero for monitoring and managing system errors

What was my part?

Within my first year, I navigated the complex landscape of transportation finance, rapidly mastering concepts like Month-End Close procedures and audit requirements while thriving in an environment with loosely defined parameters. This adaptive learning approach enabled me to architect ‘Sherlock Reporting,’ a revolutionary ML-powered system that transformed how Amazon handles clearing accounts and reconciliation at scale.

Starting with only a basic understanding of clearing accounts—temporary holders for revenue and expenses pending allocation—I collaborated with ML engineers to design intelligent systems that could process millions of daily transactions. Sherlock leverages machine learning to automate anomaly detection, predict potential reconciliation issues, and suggest corrective actions, significantly reducing manual effort in account management. By embracing ambiguity as an opportunity for innovation, I created adaptive data visualization patterns and intelligent filtering systems that evolve with usage patterns.

Through strategic analysis of existing solutions’ limitations, I identified critical gaps in handling Amazon’s unprecedented transaction volume and complexity. This insight drove the development of novel approaches specifically designed for massive-scale financial operations. I translated intricate financial requirements into intuitive user experiences, creating new paradigms for managing high-volume transportation finance workflows. The solution integrates seamlessly with TFS TransAccounting Service (TAS) for USL postings, using ML algorithms to streamline the posting process and automatically generate supporting documentation.

This synthesis of user-centered design, machine learning capabilities, and deep financial understanding resulted in a first-of-its-kind solution that not only automated complex financial workflows but also provided predictive insights for proactive account management. My ability to quickly adapt to this new domain while incorporating cutting-edge technology demonstrates my capacity to drive innovation in ambiguous, complex environments at unprecedented scale.

The results

Quantitative impact

• Built the first automated solution to shortening MEC reducing manual effort by 100 hours/month
• Achieved 30% adoption rate for completely new systems
• Created first scalable solution for handling TFS’s unique transaction volume

Organizational Impact

• Established new standards for transportation financial tools
• Demonstrated ability to rapidly master complex domains while delivering innovative solutions
• Created foundational patterns now used across multiple TFS products
• Set new benchmarks for handling high-volume transportation financial data

This pioneering work, achieved through rapid acquisition of financial expertise, not only solved immediate challenges but established entirely new paradigms for managing transportation financial systems at scale.