Data Analyst combining SQL, Python, Power BI and 4+ years of business operations experience to uncover insights, build analytics solutions, and support better decisions.
Most analysts understand data. I understand the business behind it.
With 4+ years managing data and operations at Reliance Retail, I've spent years on the ground — tracking inventory across 1,000+ SKUs, diagnosing stock variances, preparing executive-ready reports, and collaborating cross-functionally to drive operational efficiency. I know what it costs when data is wrong and what it enables when analysis is right.
I expanded into analytics and data science through structured training at OdinSchool and an internship at Labmentix Private Limited, where I built ML pipelines that delivered a 20% improvement in predictive accuracy. Today I combine both worlds: the business context to ask the right questions and the technical toolkit — SQL, Python, Power BI — to answer them with evidence.
My work doesn't stop at a dashboard. It ends at a decision.
End-to-end analytics case studies with measurable business outcomes.
A complete analytics pipeline — from raw SQL modeling to machine learning churn prediction — built on a 150K+ order dataset across 11,419 restaurants and 100K users. Identified that 80% of revenue comes from just 20% of users, and predicted 87.76% of churning customers using a Random Forest model.
What does Netflix's catalog tell us about their content strategy?
EDA across 8,000+ titles revealing content composition, genre distribution, regional patterns, and missing data insights. Delivered actionable content intelligence using Python visualization stack.
How can sales performance data drive better product and category decisions?
Cleaned and standardized raw e-commerce data, built interactive dashboards tracking revenue, AOV, and order volume trends. Category-level profitability analysis for business decision support.
How can investors visually track and compare mutual fund performance?
Combined Python-based financial analysis with Power BI interactive dashboarding to enable fund performance comparison, return analysis, and risk evaluation.
Which employees are at retention risk and which departments drive payroll costs?
Advanced SQL analysis using CTEs, Window Functions, and CASE statements to identify salary variance, retention risk, and workforce distribution across 30,000 employee records.
How can behavioral data group customers into actionable segments?
End-to-end customer segmentation using Python. Behavioral clustering to identify high-value, at-risk, and dormant customers for targeted marketing action.
Which customers are most likely to churn, and why?
Built ensemble ML models including Voting Classifier to predict churn on a 7,000+ customer telecom dataset. Identified high-risk customer segments for proactive retention.
Translating raw data into business-ready insights across the full analytics stack.
Complex multi-table queries, CTEs, window functions, and data pipeline construction from raw sources to analysis-ready models.
Multi-page Power BI dashboards with DAX measures, KPI tracking, and executive-ready reporting for operational and strategic decisions.
End-to-end EDA, data cleaning, transformation, RFM segmentation, and visualization using the full Python analytics stack.
Feature engineering, ML model development, evaluation with ROC AUC, and churn / risk prediction for business decisions.
Root cause analysis, inventory diagnostics, operational KPI design, and translating business questions into analytical frameworks.
Interactive, executive-ready dashboards with drill-through capability, dynamic filters, and clear data storytelling.
Tools and technologies I use to deliver end-to-end analytics solutions.
All projects are open source and available for review.
Full-stack analytics pipeline — SQL → Python EDA → RFM → ML churn prediction → Power BI dashboard
EDA of 8,000+ Netflix titles — content trends, genre distribution, regional patterns
HR Analytics using SQL — CTEs, Window Functions, salary variance, retention risk
Financial analytics dashboard — Python analysis with Power BI interactive dashboarding
E-commerce sales performance — revenue, AOV, category analysis
ML churn prediction — ensemble models, Voting Classifier, 84.68% accuracy
Open to Data Analyst, Business Analyst, and analytics-focused opportunities. Let's talk about how data can drive your next decision.