Products that changed how decisions get made.

Order decisions depended on fast-moving purchase-order signals and repeat manual interpretation across teams.

Partnered with data science and technology teams to take an inventory optimization agent from concept through production, translating operational rules into an automated decision workflow.

Supply-chain teams worked across fragmented dashboards, inconsistent definitions, and slow paths from question to trusted answer.

Owned strategy and execution for SCDrill, an AtScale semantic layer that standardized supply-chain metrics and created governed, reusable access for analytics and AI experiences.

Fulfillment capacity needed to respond to changing slot availability while protecting network throughput and customer commitments.

Led cross-functional delivery with supply chain, engineering, and data science to move a dynamic capacity-planning model into an operational workflow.

Engineering teams had to manually translate meeting notes and documentation into stories, pipeline changes, and validation work.

Drove adoption of a development agent that connected planning context to Jira stories, implementation changes, and automated data validation.

Core enterprise data needed greater reliability, traceability, and operating discipline to support analytics and AI.

Set product roadmaps across foundational data products, embedded business logic and ML-ready structures, and introduced repeatable quality and evaluation practices.

A complex UAV program needed stronger links between design choices, operating data, verification, and investment decisions.

Developed a lifecycle digital twin using model-based systems engineering, trade studies, and simulation to connect design and operational feedback in a traceable decision system.