Karachi, Pakistan

+92-331*******

adcq90@r.postjobfree.com

December 24, 1995

https://www.linkedin.com

/in/wali-ahmed-a770a1146

Key Technical Skills

• Languages: Python, C, R,

SQL, JavaScript, HTML, CSS

• Software: Jupyter Notebook,

Power BI, AWS, Visual

Studio, Linux Kernel,

Matlab, Git, Excel,

PowerPoint

• Data: Exploratory Data

Analysis, Regression

Analysis, Time Series

Analysis, Forecasting,

Clustering, Classification,

Segmentation, Decision

Trees, Neural Network,

Predictive Analysis

Online Courses

& Certifications

• Data Science Visualization

Harvardx edx

(Feb 2019 – March 2019)

• Data Science R Basics

Harvardx edx

(Jan 2019 – Feb 2019)

• Machine Learning

Stanford Coursera

(Nov 2017 – Jan 2018)

• Introduction to

Computer Science

(CS50) Harvard edx

(Jul 2016 – Apr 2017)

Other Interests

Football, cinematography

and automating stuff

WALI AHMED SIDDIQUI

Work Experience

PwC

Data Analyst

Karachi, Pakistan

(Jan 2019 – Present)

• Coordinate with clients and gather business requirements

• Document all KPI reports and analyses research methodology

• Involved in running complex and high volume ETL processes

• Analyzed our client’s 15 years of Primary and Secondary sales data (daily)

• Used an econometric approach with the aid of regression models to analyze price sensitivity of retailers towards multiple products

• Developed and incorporated proxies such as gross sales, product/substitute pricing, promotion and discount information, competitor pricing and market share, special events/holidays and macroeconomic factors (CPI, GDP, stock market index, exchange rate) to build price elasticity models

• Tested price elasticity models with different pricing scenarios therefore identifying optimal sales based on price changes

• Prepared Zone/SKU level forecasts (monthly and weekly) for all product lines (500+ SKUs), improving the overall supply chain efficiency by 42%

• Analyzed our client’s geospatial (GPS) data and built customized ML algorithms to infer customer behavioral indicators

• Used the inferred indicators to perform customer segmentation and hence increasing customer retention by over 25%

• Built sentiment analysis models for analyzing customer sentiment towards our client’s brand and successfully identified 38% unhappy customers Projects

Machine Learning Final Year Project: Recommendation System

(Nov 2017 – Sept 2018)

• Using graph data structure, developed an E-commerce recommendation engine

• Experimented with different machine learning techniques – Content-based Filtering, Collaborative Filtering, Bipartite Graph and Clustering for personalized recommendations

• Worked with a massive dataset of approximately 20 million records (e-commerce user data)

• Built an HTML parser (using Python’s Beautiful Soup module) to scrape product information

• Applied different evaluation techniques for testing the recommendation system Education

NED University of Engineering and Technology

Bachelor’s in Computer Systems Engineering

Karachi, Pakistan

(Dec 2014 – Dec 2018)

A levels Nixor College

Karachi, Pakistan

(Aug 2012– May 2014)

O levels Beachonhouse School System

Karachi, Pakistan

(Aug 2007 – May 2012)

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