Data Analytics
SQL, Python, Pandas, EDA, data cleaning, validation, statistical analysis, and KPI reporting.
Data & AI Analytics professional building analytics platforms, intelligent data workflows, and decision systems that turn complex data into reliable business action using Python, SQL, Tableau, FastAPI, DataHub, and Generative AI.
A practical profile focused on analytics, data platforms, AI workflows, and business impact.
Data & AI Analytics professional with an M.S. in Computer Science and a Python development background, building analytics platforms, intelligent data workflows, and decision-support systems using Python, SQL, Tableau, FastAPI, DataHub, and Generative AI to turn complex data into actionable business insights.
Analytics, data platforms, BI, and AI systems designed to turn technical data into usable decisions.
SQL, Python, Pandas, EDA, data cleaning, validation, statistical analysis, and KPI reporting.
Tableau, Power BI, executive dashboards, revenue analysis, customer insights, and stakeholder-ready reporting.
Metadata discovery, data lineage, blast-radius analysis, business context, impact scoring, and incident intelligence.
FastAPI-based analytics workflows, risk scoring, evidence-grounded recommendations, and decision-ready automation.
Gemini-powered analysis, prompt engineering, AI output validation, intelligent assistants, and automated reporting.
Six projects showing end-to-end work across data incident intelligence, business intelligence, customer analytics, retention, sports analytics, and executive decision support.
Built a DataHub-powered incident intelligence platform that traces lineage, measures blast radius and business impact, generates grounded AI recommendations, writes incident intelligence back to DataHub, verifies resolution, and preserves incident history.
Built an AI-powered business intelligence dashboard for sales analytics, customer insights, inventory tracking, marketing ROI, forecasting, AI assistant support, and executive reporting.
Analyzed 7,032 telecom customers to identify 26.58% churn, risk segments, and $1.67M annual revenue at risk.
Built an AI business analysis pipeline that cleans telecom data, engineers churn and revenue-risk features, and loads predictions into SQL reporting views.
Built a Formula 1 analytics dashboard that uses real lap-by-lap race data to analyze tire degradation, pit-stop timing, driver consistency, clean race pace, and strategy scores.
Built dashboards for $2.30M sales, $286.4K profit, 12.47% margin, orders, customers, regional performance, and AI-assisted business recommendations.
Additional work across generative AI, operations analytics, SQL reporting, content analytics, and sports performance.
Gemini-powered career analysis app that compares resumes with job descriptions and generates fit scores, skill gaps, action plans, and PDF reports.
Operations analytics project identifying delivery delays, shipping risk, profit impact, and improvement opportunities.
Relational SQL Server database and reporting project for registration trends, course capacity, and department demand.
Content analytics project uncovering trends by content type, release year, country, category, genre, and platform growth.
Sports analytics dashboard comparing player impact, efficiency, playmaking quality, and hidden-gem performance.
Skills grouped around the systems I build: analytics, BI, data platforms, APIs, AI, and business decision support.
Professional experience and education aligned with data, analytics, and AI roles.
Translate stakeholder needs into clear business requirements, analysis tasks, and concise reporting while identifying trends, gaps, and process-improvement opportunities that support data-informed decisions.
Build SQL and Python/Pandas workflows, Tableau dashboards, KPI reports, and QA-validated analytics across customer churn, revenue risk, supply chain, and business decision-support projects.
Built and tested Python backend components for REST API and data-handling workflows, debugged defects, validated outputs, documented implementation decisions, and supported front-end integration.
Graduate-level foundation in advanced data concepts, software development, and computer science.
Undergraduate foundation in data structures, algorithms, computer networks, and computer vision.