I'm a Verified Data Scientist and Analytics Engineer who builds decision-ready data systems — not just dashboards or models. I help businesses move from fragmented, slow, or unreliable data to clean pipelines, trusted metrics, and predictive insights that leaders can actually act on. My work sits at the intersection of Data Science, Business Intelligence, and Analytics Engineering — with a strong focus on structure, reproducibility, and real business impact. If your dashboards are slow, numbers don't reconcile, or insights arrive too late to matter — I fix the underlying data model, logic, and pipeline so decisions are based on facts, not guesswork. What I Deliver → BI & Analytics Engineering: KPI frameworks, semantic layers, executive dashboards, DAX optimization, and data validation systems → Applied Data Science: Forecasting, churn prediction, segmentation, anomaly detection, and explainable ML tied to business outcomes → Data Pipelines & Automation: Python + SQL pipelines for ingestion, transformation, cleansing, and QA — built for scale and reproducibility → Business & Financial Analytics: Cash-flow forecasting, ROI analysis, scenario planning, costing models, and operational KPIs → Data Integration: Excel, Google Sheets, databases, APIs, and external systems unified into a single analytics layer How I Work Every engagement starts with clear problem framing and success metrics. My delivery is scope-driven, milestone-based, and fully documented — built for clean handover and long-term usability. I focus on analytics people can trust, understand, and act on. GitHub: [login to view URL]