Neeraj

Data Analyst

Delhi, India 0+ yrs exp 86 · Excellent

About

Data Analyst with hands-on experience in Python, SQL, Power BI, Microsoft Excel, data visualization, dashboard development, and KPI reporting. Built end-to-end analytics projects using telecom, insurance, and retail datasets to deliver business insights.

Skills & Expertise (31)

Data Cleaning Intermediate

6.0/10

Years Exp

Data Visualization Intermediate

6.0/10

Years Exp

Interactive Dashboards Intermediate

6.0/10

Years Exp

Pivot Tables Intermediate

6.0/10

Years Exp

Dashboards Intermediate

6.0/10

Years Exp

KPI Analysis Intermediate

5.5/10

Years Exp

duplicate detection Intermediate

5.5/10

Years Exp

CASE WHEN Intermediate

5.5/10

Years Exp

Subqueries Intermediate

5.5/10

Years Exp

Aggregations Intermediate

5.5/10

Years Exp

Window Functions Intermediate

5.5/10

Years Exp

CTEs Intermediate

5.5/10

Years Exp

Joins Intermediate

5.5/10

Years Exp

Advanced Formulas Intermediate

5.5/10

Years Exp

Power Query Intermediate

5.5/10

Years Exp

XLOOKUP Intermediate

5.5/10

Years Exp

VLOOKUP Intermediate

5.5/10

Years Exp

Conditional Formatting Intermediate

5.5/10

Years Exp

Pandas Intermediate

5.5/10

Years Exp

Business Intelligence Reporting Intermediate

5.5/10

Years Exp

Trend Analysis Intermediate

5.5/10

Years Exp

Business Requirements Gathering Intermediate

5.5/10

Years Exp

Root Cause Analysis Intermediate

5.5/10

Years Exp

Data Storytelling Intermediate

5.5/10

Years Exp

KPI development Intermediate

5.5/10

Years Exp

Exploratory data analysis Intermediate

5.5/10

Years Exp

Feature Engineering Intermediate

5.0/10

Years Exp

Data Modeling Intermediate

5.0/10

Years Exp

DAX measures Intermediate

5.0/10

Years Exp

Row-Level Security Intermediate

5.0/10

Years Exp

Query Optimization Intermediate

5.0/10

Years Exp

Work Experience

Data Analyst

Customer Shopping Behavior Analysis

Aug 2026 - Sep 2026

Cleaned and analyzed 3,900 retail transactions using Python, SQL, and Power BI, uncovering revenue and behavior patterns across gender, age, and category. Found male customers generated 2x the revenue of female customers ($157,890 vs $75,191), and Clothing alone drove $104K, more than the other three categories combined. Wrote 10 SQL queries to segment customers by loyalty and spend, finding the Loyal segment made up most of the customer base while only 27% were subscribers. Built a Power BI dashboard showing Young Adults drove the highest age-group revenue ($62K), and Hats, Sneakers, and Coats had the highest discount reliance (~50%). Recommended reworking the subscription program and protecting margins on high-discount categories, since subscription status showed no real link to higher spend.

Data Analyst

Customer Churn Analysis

Mar 2026 - May 2026

Analyzed 6,418 customer records to find what was driving a 27% churn rate across contract type, tenure, services, and geography. Found month-to-month customers churn at 46.5%, compared to just 2.7% on two-year contracts, and recommended long-term contract incentives. Showed customers without Online Security churn at 84.6% vs 15.4% with it, and flagged competitor pricing as the top reason for churn (43.9%). Recommended pushing customers toward longer contracts and bundling in Online Security, since both were directly tied to who was leaving and who wasn't.

Data Analyst

Insurance Risk & Claims Analysis

Jan 2026 - Mar 2026

Built a Power BI dashboard covering 37,542 insurance policies and $187.8M in claims, broken down by car type, age group, education, and coverage zone. Found private vehicles generate 4x more claims than commercial ones ($150.4M), with Ford and Chevrolet alone accounting for $32M. Identified single, High School educated policyholders as the highest-risk group ($40.2M in claims), and noticed claims drop sharply for cars made after 2015. Suggested the company charge higher premiums for private vehicle owners, especially single policyholders with only a high school education, since this group filed the most claims by far.

Data Analyst

Sales Store Analysis

Nov 2025 - Jan 2026

Analyzed 2,000+ retail transactions across 472 customers, 30 products, and 6 categories for 2023, covering ₹5.9Cr+ in revenue, using SQL in SSMS. Cleaned the data by removing 4 duplicate records and fixing NULLs across all 14 columns, and standardized gender labels and payment modes. Used CTEs, joins, and window functions to answer 10 business questions, and found the top 5% of products drove ~40% of total profit. Advised the store focus stock and offers around its busiest hours and top-selling age group, since that's where most of the actual revenue was coming from.