I turn messy data into decisions that save companies money and grow revenue.
- 🚗 Uncovered a ₹2.3Cr monthly GMV loss in Uber's cancellation data
- 📈 Forecasted $192K Q1 revenue using ARIMA time-series modeling
- 👥 Segmented 2,205 customers with K-Means to reveal disengaged high-spenders
- 🎓 Google Data Analytics Professional Certificate
🎯 Open to entry-level Data Analyst / MIS Analyst roles — available from August 2026
Built an end-to-end fraud detection pipeline analyzing 1,00,000 transactions
across 50 fuel stations in 3 cities — uncovered 27.5% fraud rate, 16,200L
under-dispensing, and proved 3 business hypotheses on customer retention
using sensor data validation.
Tech Stack: Python, PostgreSQL, Power BI, DAX
End-to-end fintech analytics project on Buy Now Pay Later lending data —
cleaned and analyzed loan-level data in Python, then built a Power BI
dashboard segmenting customers into High/Medium/Low credit risk tiers,
tracking default rate, and surfacing which merchant categories and
providers drive the highest loan volume and risk exposure.
Tech Stack: Python, Jupyter Notebook, Power BI, DAX
ML pipeline on 7,043 customers predicting 26.54% churn — trained 4 models
with SMOTE balancing; Random Forest selected as optimal.
Tech Stack: Python, Pandas, scikit-learn
Simulated a Senior Product Analyst role at Uber — analyzed 200K NYC rides to
diagnose 3 business problems: rider retention (only 19% active by Day 30),
early-morning cancellation leakage, and surge pricing impact. Built cohort
retention SQL, ran a simulated A/B test on surge caps, and segmented riders
via KMeans clustering.
Tech Stack: Python, SQL (PostgreSQL), Power BI
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Programming & Databases
Libraries & Analytics
BI & Reporting
Tools
💬 Got a role, a project, or just want to talk data? Reach out on LinkedIn — always happy to connect.