Most companies have thousands of transaction rows sitting in their databases, yet executives still fly blind when it comes to answering critical questions about customer churn, profit leaks, and operational bottlenecks. I am a Business Intelligence Specialist and Data Analyst who bridges the gap between messy raw data and executive strategy. I don't just build charts; I engineer automated data pipelines and interactive dashboards that allow founders to stop guessing and start making data-driven decisions instantly. How I Drive Value: • Diagnostic Analytics: Uncovering the "why" behind the numbers (Cohort retention, RFM segmentation, churn analysis). • Data Engineering: Writing complex, optimized SQL queries (CTEs, Window Functions) and Python scripts (Pandas) to clean messy datasets for production. • Enterprise Dashboards: Designing production-ready Power BI reporting systems with custom DAX time-intelligence and automated executive summaries. Proof of Work: Recently, I ran a diagnostic on a massive 113,000-transaction e-commerce dataset. By engineering custom logistics metrics, I uncovered a hidden 96.9% churn rate driven entirely by shipping bottlenecks—shifting the executive focus from blind customer acquisition to highly profitable retention. Let's stop relying on static Excel files and build a data infrastructure that scales with your business.

Zeeshan Akram | BI Specialist

Most companies have thousands of transaction rows sitting in their databases, yet executives still fly blind when it comes to answering critical questions about customer churn, profit leaks, and operational bottlenecks. I am a Business Intelligence Specialist and Data Analyst who bridges the gap between messy raw data and executive strategy. I don't just build charts; I engineer automated data pipelines and interactive dashboards that allow founders to stop guessing and start making data-driven decisions instantly. How I Drive Value: • Diagnostic Analytics: Uncovering the "why" behind the numbers (Cohort retention, RFM segmentation, churn analysis). • Data Engineering: Writing complex, optimized SQL queries (CTEs, Window Functions) and Python scripts (Pandas) to clean messy datasets for production. • Enterprise Dashboards: Designing production-ready Power BI reporting systems with custom DAX time-intelligence and automated executive summaries. Proof of Work: Recently, I ran a diagnostic on a massive 113,000-transaction e-commerce dataset. By engineering custom logistics metrics, I uncovered a hidden 96.9% churn rate driven entirely by shipping bottlenecks—shifting the executive focus from blind customer acquisition to highly profitable retention. Let's stop relying on static Excel files and build a data infrastructure that scales with your business.

Available to hire

Most companies have thousands of transaction rows sitting in their databases, yet executives still fly blind when it comes to answering critical questions about customer churn, profit leaks, and operational bottlenecks.

I am a Business Intelligence Specialist and Data Analyst who bridges the gap between messy raw data and executive strategy. I don’t just build charts; I engineer automated data pipelines and interactive dashboards that allow founders to stop guessing and start making data-driven decisions instantly.

How I Drive Value:
• Diagnostic Analytics: Uncovering the “why” behind the numbers (Cohort retention, RFM segmentation, churn analysis).
• Data Engineering: Writing complex, optimized SQL queries (CTEs, Window Functions) and Python scripts (Pandas) to clean messy datasets for production.
• Enterprise Dashboards: Designing production-ready Power BI reporting systems with custom DAX time-intelligence and automated executive summaries.

Proof of Work:
Recently, I ran a diagnostic on a massive 113,000-transaction e-commerce dataset. By engineering custom logistics metrics, I uncovered a hidden 96.9% churn rate driven entirely by shipping bottlenecks—shifting the executive focus from blind customer acquisition to highly profitable retention.

Let’s stop relying on static Excel files and build a data infrastructure that scales with your business.

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Language

English
Fluent
Urdu
Fluent

Work Experience

Data Science & Analytics Intern at DevelopersHub Corporation
June 10, 2025 - July 26, 2025
In this role, I engineered an interactive 'Superstore Sales Dashboard' to track regional profitability and KPIs. I conducted deep exploratory analysis on telecom and e-commerce datasets, diagnosing customer churn and user purchasing behaviors, while building reusable Python scripts for data cleaning and automation.

Education

BS Software Engineering at Virtual University of Pakistan
August 19, 2024 - August 19, 2028
Present Focus: Database Management Systems (DBMS), Data Structures, Business Intelligence.

Qualifications

IBM Databases & SQL for Data Science
January 11, 2030 - March 15, 2026
Data Analysis with Python
January 11, 2030 - March 15, 2026
Data Visualization with Python
January 11, 2030 - March 15, 2026
Google Crash Course on Python
January 11, 2030 - March 15, 2026

Industry Experience

Software & Internet, Telecommunications, Consumer Goods, Media & Entertainment
    uniE608 Title: Diagnosing a 96.9% E-Commerce Churn Rate (113k+ Orders)
    The Business Problem Most e-commerce founders obsess over 5-star reviews, assuming satisfaction equals loyalty. I conducted an end-to-end diagnostic analysis on a massive dataset of 113,000+ marketplace transactions to test this assumption. The data revealed a staggering 96.9% customer churn rate across the board—and shockingly, customers who left perfect 5-star reviews churned at nearly the exact same rate as those who left 1-star reviews. My Strategy & Execution I engineered a complete data pipeline to uncover the actual root cause of the revenue leak: Data Engineering (Python): Cleaned and merged 9 fragmented datasets using Pandas, creating new predictive features like delivery_delta to track estimated vs. actual shipping times. Diagnostic Analytics (SQL): Replicated a complex RFM (Recency, Frequency, Monetary) segmentation model using advanced SQL Window Functions to isolate high-value user cohorts. Executive Reporting (Power BI): Designed a 4-page, interactive dashboard with custom DAX and geographic mapping to present the findings to stakeholders. Key Discoveries & Business Value Logistics is Marketing: By visualizing the delivery_delta, I proved that logistics inconsistency—not pricing—was driving churn. A delay of just a few days instantly halved review scores and destroyed repeat purchase probability. The "Promising" Cohort: The RFM model successfully isolated a specific cohort of users with a 30% higher Average Order Value (AOV), allowing the business to shift marketing spend from blind acquisition to targeted retention. Technical Stack Used Database & Querying: Advanced SQL (PostgreSQL, Window Functions, CTEs) Data Manipulation: Python (Pandas, NumPy) Visualization & BI: Power BI, Custom DAX

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