Data Analyst · Business Intelligence · Risk & Retail Analytics
SQL | Python | Power BI | Excel | ETL
I'm a Data Analyst based in Kolkata with 7+ years of operational leadership experience, now fully focused on turning raw data into decisions that protect revenue and reduce risk.
I don't just build dashboards — I ask why the numbers look the way they do. My projects cover fraud detection, revenue concentration analysis, customer segmentation, and retail pricing strategy — all built with real datasets and documented end-to-end.
- 🔍 Currently working on: Lowe's-style Retail Merchandise Analysis (Power BI + SQL)
- 📊 Certified: IBM Data Analysis · Cisco Data Analytics · SAS SQL Essentials · Aptech Smart Data Analytics (Distinction, 86%)
- 📍 Based in Kolkata · Open to Data Analyst / Business Analyst roles
Skill Badges
Stack: Python · SQL · Excel · Power BI
An end-to-end SaaS analytics solution built using Python, SQL Server, Excel, and Power BI to transform raw subscription data into actionable business insights through data engineering, analytics, and interactive dashboards.
Stack: Python · SQL · Excel · Power BI
Retail merchandising analytics project featuring KPI tracking, category performance analysis, sales trend visualization, and business insights using Power BI, SQL, Excel and Python.
Stack: Python · SQL · Excel · Power BI
Built an end-to-end ETL pipeline consolidating multi-source e-commerce data. Uncovered 65–70% revenue concentration risk in top categories and built KPI dashboards (Revenue, AOV, Orders) that reduced time-to-insight by ~30%.
Stack: Python · Power BI
Designed a fraud detection workflow identifying peak fraud windows (12–4 AM). Automated risk segmentation reduced manual review effort by ~30%. Multi-page Power BI report with drill-through from executive summary to transaction level.
Stack: SQL (CTEs, Window Functions)
Applied RFM modelling to segment customers into 5 tiers. Surfaced the top ~20% of users driving ~80% of revenue, enabling targeted retention strategy.
Stack: Python (Pandas, Matplotlib, Seaborn)
EDA on hotel pricing and demand data. Identified 60%+ mid-range segment dominance and strong demand below ₹2,000/night. Segmented listings into 4 pricing tiers, improving pricing strategy clarity by ~25%.
| Category | Tools |
|---|---|
| Languages | Python (Pandas, NumPy, Matplotlib, Seaborn) · SQL |
| BI & Dashboards | Power BI (DAX, Power Query) · Excel (Pivot, VLOOKUP) |
| Analytics | ETL · EDA · RFM Analysis · KPI Tracking · MIS Reporting |
| Concepts | Data Modeling · Business Intelligence · Statistical Analysis |
If you're hiring for Data Analyst or Business Analyst roles in Kolkata — or remotely — I'd love to talk.
📧 mdyusuf911@gmail.com | 🔗 LinkedIn