Product Manager building AI-enabled operations, data platforms, workflow automation, and B2B integrations
Portfolio · Product case studies · Product Leader Radar Demo · Resume Tailor Demo
I turn complex, manual workflows into measurable products. My work sits at the intersection of product strategy, operational systems, data, and applied AI—especially where technical decisions directly affect customer experience and business performance.
I currently build internal healthcare operations products at BlinkRx. Previously, I worked across B2B SaaS, insurance, consulting, and data products. I am a Carnegie Mellon MISM graduate with a background in computer engineering.
- Built a workforce-performance system of record that informs 100+ daily capacity and staffing decisions across six teams.
- Improved labor-model accuracy from approximately 70% to 92% by bringing previously untracked work into the operating platform.
- Helped reduce production incidents by approximately 35% and resolution time by approximately 40% through reliability improvements and AI-assisted diagnosis.
- Shipped self-serve data-import and onboarding workflows and reusable B2B integration patterns.
- Built a private, human-reviewed product-research operating system with explicit evidence provenance, cost controls, and failure isolation.
| Project | What it demonstrates |
|---|---|
| Product case studies | Product decisions, trade-offs, cross-functional execution, and measurable outcomes under confidentiality constraints |
| Product Leader Radar Demo | A synthetic, network-free demonstration of evidence-aware qualification, deduplication, scoring, and human-review controls |
| Resume Tailor Demo | Explainable matching between role requirements and verified, fictional resume evidence—without generated claims |
| CSV Splitter | A focused self-serve tool designed around a real operational file-size constraint |
| Accident Alert System | An early end-to-end prototype connecting mobile software, vehicle telemetry, location, and emergency workflows |
| Agriculture IoT | A mobile control prototype for SMS-connected field configuration, filtration, and fertigation workflows |
- Start with the user, operating context, and measurable problem.
- Make assumptions and trade-offs explicit.
- Align design, engineering, operations, and commercial stakeholders around outcomes.
- Ship in increments, instrument the workflow, and learn from real behavior.
- Use prototypes and technical exploration to reduce uncertainty—not as substitutes for discovery.
Portfolio figures are rounded, confidentiality-reviewed outcomes. Disputed or insufficiently defined metrics are withheld rather than presented with false precision. No confidential employer data, internal documents, or proprietary implementation details are included here.