Python • SQL • Power BI • Simulation • Optimization • Reproducible Analytics
I build analytics and decision-support systems that transform operational, commercial, marketplace, and supply-chain data into KPIs, dashboards, simulations, risk assessments, and actionable business recommendations.
My portfolio connects Business Analytics, Operations and Decision Intelligence, and Analytics and Research Engineering through reproducible projects grounded in real-world data, quantitative methods, and executive communication.
KPI development, executive reporting, dashboard design, revenue analytics, marketplace intelligence, operational risk, cost analysis, and business insight generation.
Supply-chain analytics, simulation, optimization, scenario analysis, policy benchmarking, resilience evaluation, and data-driven decision support.
Reproducible Python pipelines, automated validation, testing, GitHub Actions, Numba acceleration, evidence provenance, and research workflow automation.
| Project | Business Value | Core Capabilities |
|---|---|---|
| Marketplace Intelligence Platform | Customer, seller, category, delivery, and marketplace-risk intelligence | Marketplace Analytics • BI • Network Science • Intervention Ranking |
| CRM Revenue Intelligence Dashboard | Revenue, conversion, product, sector, and sales-performance reporting | CRM Analytics • Revenue KPIs • Executive Dashboards |
| Operational Risk & Reliability Analytics | Failure probability, severity, utilization, and cost-exposure analysis | Python • SQL • Power BI • Risk Analytics |
| Shipment Pricing Analytics | Freight-cost, shipment-mode, country, and cost-per-kg analysis | SQL • Power BI • Logistics Analytics • KPI Development |
| Project | Decision Problem | Core Capabilities |
|---|---|---|
| Supply Chain Digital Twin | Evaluate disruptions, policies, critical nodes, and resilience investments | Simulation • Optimization • Network Science • Executive Reporting |
| Demand Forecasting | Compare forecasting models for inventory and supply planning | Python • Feature Engineering • Model Evaluation |
| Route Optimization | Reduce route distance while respecting fleet capacity | Operations Research • Heuristics • Visualization |
| Inventory Simulator | Evaluate inventory policies and service-level tradeoffs | Inventory Analytics • Simulation • KPI Reporting |
| Project | Contribution | Core Capabilities |
|---|---|---|
| Industrial Research Automation Lab | Evidence-linked workflow from literature retrieval to reproducible computational validation | Python • Numba • pytest • CI/CD • GitHub Actions • Evidence Provenance |
| Near-Critical Systems Research | First-passage reliability, corrected approximation, hazard characterization, and threshold control | Monte Carlo • Statistical Inference • Stochastic Modeling • Reproducible Research |
| Controlled Near-Critical Benchmark | Controlled experiments connecting near-critical theory with industrial application | Threshold Policies • Critical Boundaries • Benchmarking • DOI Archive |
| Project | Focus |
|---|---|
| Phi Rectangles | Mathematical modeling, Fibonacci ratios, loops, functions, and visualization |
| Fibonacci Geometry Experiments | Early Python geometry experiment retained for planned redevelopment |
Near-Critical Systems (Paper A)
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Controlled Near-Critical Benchmark (Paper B)
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Supply Chain Digital Twin application
This research line connects foundational stochastic theory, a reproducible controlled benchmark, and an industrial Digital Twin application for supply-chain resilience and decision intelligence.
A parallel Industrial Research Automation Lab provides the engineering infrastructure for deterministic experiments, statistical validation, evidence traceability, CI/CD, and bounded research workflows.
| Area | Capabilities |
|---|---|
| Business Analytics & BI | KPI Development • Executive Reporting • Dashboard Design • Data Visualization • Business Insights |
| Domain Analytics | Supply Chain • Logistics • Marketplace • CRM / Revenue • Operational Risk |
| Decision Support | Scenario Analysis • Policy Benchmarking • Cost Analysis • Process Improvement • Resilience Evaluation |
| Quantitative Methods | Monte Carlo Simulation • Statistical Inference • Operations Research • Optimization • Network Science |
| Analytics Engineering | Reproducible Pipelines • Testing • Automated Validation • Artifact Generation • Version Control |
| Research Engineering | Numba Acceleration • GitHub Actions • CI/CD • Evidence Provenance • Research Automation |
Python • SQL • Power BI • Pandas • NumPy • Numba • SciPy • NetworkX • Matplotlib • SQLite • pytest • GitHub Actions
Focused on opportunities in Business Analytics, Business Intelligence, Operations Analytics, Supply Chain Analytics, and Decision Intelligence.
My portfolio also demonstrates transferable capabilities in analytics engineering, quantitative analysis, simulation, optimization, and research automation, supported by a logistics and operations background.