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Proof of Impact

I don't just talk about what I can do—I show you exactly how I've done it. My work isn't just a collection of tech demos; it's Proof of how I turn business problems into measurable profit.

StoreCast: Production-Grade Forecasting System

Self-Initiated Project

The Problem:

Using real retail data, I simulated a 45-store chain bleeding margin from an 11.85% forecasting error—resulting in $216M trapped in safety stock, constant stockouts, and operational chaos.

The Solution:

Built a production-ready MLOps pipeline (Polars, DuckDB, XGBoost) that slashed forecast error to 7.76%—with automated retraining, drift monitoring, and real-time dashboards.

The Impact:
  • $9.61M in projected annual profit growth (reduced stockouts and optimized markdowns)
  • $20.53M in freed working capital (9.5% less safety stock needed)
  • 320 hours/month saved in manual labor via automation

What This Proves: This project demonstrates the exact methodology I bring to client engagements—from feasibility research to production deployment using real-world messy data.

COVID-19 Decision Intelligence & Risk Monitoring

Automated National-Level Situational Reporting

Public Health & Automation

The Problem:

Fragmented data across 36 states was taking 6+ hours to process manually, creating a 2-day lag before leadership received actionable strategic briefings.

The Solution:

Engineered an end-to-end Medallion pipeline in PostgreSQL, integrated with n8n orchestration and Google Gemini/Gemma LLMs for automated synthesis.

The Impact:
  • <30s ETL runtime, eliminating 6+ hours of manual labor
  • 95% faster decision-making (reports generated in <60 seconds)
  • 80% reduction in administrative monitoring via AI-driven Hot Zone isolation

What This Proves: I don't just build pipelines; I focus on data velocity and synthesizing complex data into executive-ready insights.

Flights Price Prediction MLOps

Zero-Overhead Multi-Cloud Deployment Pipeline

MLOps & CI/CD

The Problem:

Manual deployments, environment discrepancies ("it works on my machine"), and zero traceability for model versions and data.

The Solution:

Architected a multi-cloud CI/CD workflow with GitHub Actions, AWS for centralized MLflow tracking, and serverless deployment on Google Cloud Run.

The Impact:
  • 100% reduction in manual deployment overhead
  • $7.60 RMSE achieved with a highly optimized LightGBM model
  • Complete Reproducibility via strict DVC data versioning and multi-stage Docker builds

What This Proves: I ensure machine learning models actually reach production safely, securely, and reliably.

Your Problem Is Different—That's the Point

I don't claim to have solved your exact challenge before. What I do have is a battle-tested process that works across industries.

Here's how I'd approach your problem:

1. Listen First

I dig into your constraints, KPIs, and what's already been tried. No copy-paste solutions.

2. Research Your Domain

I study your industry and identify 2-3 possible approaches (ML, automation, or simple heuristics).

3. Prove Feasibility First

You get an optimization report with rough impact estimates—before I write production code.

4. Build & Deliver

End-to-end MLOps pipeline with monitoring, documentation, and handoff. You own it.

Proven Methodology

Same rigorous process. Different data. Proven methodology.

Ready to see how this applies to your business?

Whether you're in logistics, SaaS, manufacturing, or something entirely different.

Book a Discovery Call