g janani

Data Analyst

Bengaluru, Karnataka, India 85 · Excellent

About

Data Analyst with an MSc Agricultural Statistics student with hands-on experience in SQL, Advanced Excel, Power BI, Python, and reporting automation. Skilled in data cleaning, EDA, KPI analysis, dashboard development, data validation, statistical analysis, forecasting, and business reporting. Experienced in automating MIS workflows using Excel, VBA, Macros, and Power Query and developing data-driven dashboards to identify trends and business insights.

Skills & Expertise (35)

SQL Advanced

8.5/10

Years Exp

Python Advanced

8.5/10

Years Exp

Analytical Thinking Advanced

8.0/10

Years Exp

Data Visualization Advanced

8.0/10

Years Exp

Data Cleaning Advanced

8.0/10

Years Exp

Problem Solving Advanced

8.0/10

Years Exp

Power BI Advanced

8.0/10

Years Exp

Tableau Intermediate

6.5/10

0.5

Years Exp

Attention to Detail Communication Stakeholder Collaboration Business understanding Regression Analysis Descriptive Statistics Presentation Report Automation Data Quality Analysis Anomaly Detection Forecasting Trend Analysis Statistical Analysis Business Reporting Dashboard Development KPI Analysis Exploratory data analysis Data Validation Seaborn Matplotlib NumPy Pandas GitHub Git VBA Power Query Microsoft Excel

Work Experience

Customer Behavior Analytics

RetailRocket

Sep 2026 - Sep 2026

Analyzed 2.75M+ e-commerce events using Python and SQL, revealing just 0.83% purchase conversion and highlighting a major customer funnel gap. Identified 27,146 cart non-converters through funnel analysis, revealing a significant pool of high-intent customers for cart-recovery opportunities. Analyzed category and time-based behavior using SQL and Power BI, identifying 95% abandonment in the highest-drop-off category and 84.94% at 9 AM.

Sales Performance Analysis

Amazon

Jul 2026 - Jul 2026

Analyzed 128K+ orders using Python and SQL, uncovering ₹78.59M in revenue and key trends across categories, cities, and monthly sales. Identified Set and Kurta as the top-performing categories, generating ₹60.50M (~77%) of total revenue, supporting focused category-level sales planning. Built an interactive Power BI dashboard with dynamic filters and KPIs to monitor revenue, orders, fulfillment, category, city, and monthly performance for faster business reporting.

Machine Learning

Customer Churn Prediction

Jul 2026 - Jul 2026

Extracted lifecycle profiles for 7,043 clients via PostgreSQL and built a predictive pipeline in Python, including feature engineering and missing-value resolution. Evaluated predictive performance across classification models, achieving an optimal baseline with a Logistic Regression model (82.30% accuracy, 0.845 AUC score). Translated model feature weights into clear risk matrices, giving non-technical stakeholders early-warning churn indicators to protect at-risk revenue.