Business Intelligence • SQL • Python • Power BI • Data Pipelines • Decision Intelligence • AI-Augmented Analytics Automation
I build analytics and decision-support systems that transform operational, commercial, marketplace, CRM, and supply-chain data into KPIs, dashboards, automated workflows, simulations, risk assessments, and executive-ready business recommendations.
My current work is centered on AI-native analytics engineering: using artificial intelligence as a technical execution infrastructure to industrialize analytical production across SQL, Python, Power BI, DAX, ETL, dashboards, documentation, validation, and reproducible data workflows.
I have moved beyond the traditional manual analytics development model, where productivity is limited by syntax memorization, formula recall, or step-by-step operation inside specific tools. I use AI systems as an integrated technical execution layer: an instant expert knowledge base, code generation engine, logical architecture adviser, validation assistant, documentation accelerator, and high-speed analytical production force.
This does not mean replacing analytical judgment with automation. My role is to define the business problem, structure the KPI logic, provide data context, direct AI-assisted iterations, test outputs, validate results, correct inconsistencies, and turn generated work into functional, reproducible, and scalable analytical systems.
In practice, I use this workflow to convert business objectives and real datasets into:
- Python scripts for cleaning, modeling, visualization, automation, simulation, and reporting.
- SQL queries for joins, aggregations, KPI logic, analytical datasets, validation checks, and reporting layers.
- DAX and Power Query logic for BI models, measures, transformations, and executive dashboards.
- ETL and data pipeline workflows for extraction, transformation, validation, documentation, and repeatable execution.
- Tool-agnostic analytics solutions that can be implemented through Power BI, SQL, Python, dashboards, notebooks, reports, or automated pipelines depending on the business problem.
This methodology transforms manual BI and data-analysis work into industrialized, AI-augmented, human-validated analytics production focused on speed, traceability, quality, and decision-making.
KPI development, executive reporting, dashboard design, data visualization, revenue analytics, marketplace intelligence, operational risk, commercial analytics, cost analysis, and business insight generation.
AI-assisted SQL, Python, DAX, ETL design, dashboard prototyping, KPI generation, analytical documentation, data validation, reproducible workflows, and automated reporting pipelines.
Supply-chain analytics, logistics analytics, simulation, optimization, scenario analysis, policy benchmarking, resilience evaluation, and data-driven decision support.
Reproducible Python pipelines, automated validation, testing, GitHub Actions, CI/CD, Numba acceleration, evidence provenance, deterministic computation, and research workflow automation.
| Project | Business Value | Core Capabilities |
|---|---|---|
| Marketplace Intelligence Platform | Customer, seller, category, delivery, revenue, and marketplace-risk intelligence | Marketplace Analytics • BI • Network Science • Intervention Ranking • AI-Augmented Analytics |
| CRM Revenue Intelligence Dashboard | Revenue, conversion, product, sector, and sales-performance reporting | CRM Analytics • Revenue KPIs • Executive Dashboards • AI-Assisted Reporting |
| Operational Risk & Reliability Analytics | Failure probability, severity, utilization, and cost-exposure analysis | Python • SQL • Power BI • Risk Analytics • Validation Workflows |
| 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 • Decision Intelligence |
| 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 • Automation |
| 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
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Industrial Research Automation Lab
This research line connects foundational stochastic theory, reproducible controlled benchmarking, and industrial Digital Twin applications for supply-chain resilience and decision intelligence.
The parallel Industrial Research Automation Lab provides the engineering infrastructure for deterministic experiments, statistical validation, evidence traceability, CI/CD, artifact integrity, and bounded research workflows.
| Area | Capabilities |
|---|---|
| AI-Native Analytics | AI-Augmented Analytics Engineering • Prompt Engineering for Data Workflows • LLM-Assisted SQL/Python/DAX • Tool-Agnostic Analytics Execution |
| Business Analytics & BI | KPI Development • Executive Reporting • Dashboard Design • Data Visualization • Business Insights |
| Data Engineering & Automation | Data Pipelines • ETL • Automated Validation • Documentation • Reproducible Workflows |
| 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 |
| Research Engineering | Numba Acceleration • GitHub Actions • CI/CD • Evidence Provenance • Research Automation |
Python • SQL • Power BI • DAX • Power Query • Pandas • NumPy • Numba • SciPy • NetworkX • Matplotlib • SQLite • pytest • GitHub Actions • CI/CD • AI-Assisted Development • Prompt Engineering
Focused on opportunities in Data & Analytics Engineering, Business Intelligence, Power BI Development, Analytics Engineering, Senior Data Analysis, Operations Analytics, Supply Chain Analytics, and AI-Augmented Data Workflows.
My portfolio demonstrates transferable capabilities across analytics engineering, quantitative analysis, simulation, optimization, BI automation, reproducible workflows, and AI-native analytical production, supported by a logistics and operations background.