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

End-to-end SaaS analytics solution covering MRR, ARR, churn, expansion, contraction, cohort analysis, CAC, LTV and executive KPI reporting. Built with SQL, Python, Power BI and advanced DAX.

Stack: Python · SQL · Excel · Power BI

End-to-end retail merchandising analytics system with star schema modelling, vendor analysis, promotional lift evaluation and merchant dashboards built for decision support.

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

A BFSI-focused monitoring system flagging anomalous transaction patterns for risk and compliance teams — built to reduce fraud losses and false-positive investigation time. Designed a fraud detection workflow identifying peak fraud windows (12–4 AM). Automated risk segmentation reduced manual review effort by ~30%.

Stack: SQL (CTEs, Window Functions)

SQL-driven customer segmentation framework that identifies high-value customer groups for targeted marketing and improved retention. 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)

Optimizing Occupancy Rates & Pricing Strategy Through Market-Level Exploratory Analysis: Operational insights on demand patterns and pricing sensitivity to inform revenue management decisions in the hospitality sector. 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%.

If you're hiring for Data Analyst or Business Analyst roles in Kolkata — or remotely — I'd love to talk.

📧 mdyusuf911@gmail.com  |  🔗 LinkedIn