Case Study: EDA Data Platform
Operationalizing governed plant data for enterprise analytics and decision velocity.
Project Snapshot
- Role: Lead Data Scientist / Platform Architect
- Domain: Manufacturing analytics and governance
- Stack: Azure ML, Snowflake/Snowpark, Python APIs, MLOps
- Timeline: 2022 – Oct 2025 (enterprise delivery phase)
16+ Hours/Week Reclaimed
Program-level analytics tooling reduced recurring engineering and administrative effort by more than 16 hours per week (public-shareable resume metric).
5% → 1% Defect Drift
Data-informed quality programs reduced nuisance defect rates from 5% to 1% in targeted manufacturing workflows (program-level outcome).
>10% Yield Lift
Root-cause and process optimization efforts supported by governed analytics delivered more than 10% yield improvement in a one-year window.
Quantified Outcomes (Public-Shareable)
- 16+ hours/week of engineering and administrative effort reclaimed through analytics automation patterns.
- 5% to 1% nuisance defect-rate shift in targeted quality workflows using stronger data feedback loops.
- >10% yield improvement delivered in a one-year optimization window where governed analytics informed interventions.
Problem
Manufacturing stakeholders needed reliable, timely, and consistent access to process data, but data was fragmented across systems and teams. This slowed troubleshooting, benchmarking, and adoption of advanced analytics.
Approach
I led design and deployment of a governed EDA platform composed of ingestion pipelines, validation rules, and API-based delivery. The architecture balanced plant usability, IT governance, and analytical flexibility.
Outcome
The platform became a core analytics layer for multiple initiatives, enabling faster root-cause analysis and more consistent reporting across operations. It also created the technical baseline for next-gen applied AI use cases and informs my post-Oct 2025 focus on agentic workflows and production programming systems.