Hello!

I'm Rameen Shahid



Data Analytics, Business Intelligence, and Data Visualization expert

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Name: Rameen Shahid

Job Role: Senior Data Analyst

Experience: 2 Years 3 Months

Address: Peshawar, Pakistan

Skills

SQL 95%
PYTHON 85%
Data Visualization 90%
Statistical Analysis 85%
Machine Learning 80%

About

About Me

I'm Rameen Shahid, a data enthusiast with two years of experience in data science. With over 2 years of comprehensive experience in the field of data science & analytics, accompanied by a bachelor's degree in Mathematics. Proficient in data analysis, statistical analysis, hypothesis testing, customer behaviour analysis, & machine learning. Demonstrated success in leading impactful projects and providing effective mentorship. I specialize in using SQL, MySQL, Tableau, Power BI, and Grafana to uncover insights and create captivating visualizations. Let's transform data into meaningful, useful stories that inspire and innovate together!

  • Profile: Data Science & Analytics
  • Domain: Retail, Ecommerce, BFSI & Digital Marketing
  • Education: Bachelor of Mathematics
  • Language: English, Urdu
  • BI Tools: Microsoft Power BI, Looker & Tableau
  • Other Skills: Cloud, PySpark, Excel, Git, JIRA, Google Analytics & SEO
  • Interest: Traveling, Travel Photography, Teaching

0 +   Projects completed

LinkedIn

Resume

Resume

As a dedicated Data Analyst with two year 2 of professional experience in data analysis, I excel in leveraging statistical techniques and advanced tools to extract valuable insights from complex datasets. I am passionate about translating data into actionable strategies that drive business growth and innovation. Seeking opportunities to apply my analytical skills and contribute to impactful projects within a dynamic and forward-thinking organization

Experience


Until September 2024

Jr. Data Scientist

AptechSoft

At AptechSoft, I worked as a Data Science Intern, where I gained hands-on experience in machine learning, data analysis, and predictive modeling. My key contributions included:

  • Developed predictive models using scikit-learn, TensorFlow, and XGBoost to improve forecasting accuracy.
  • Built and deployed machine learning algorithms for supervised and unsupervised learning with scikit-learn and PyTorch.
  • Performed data cleaning and preprocessing using Pandas and NumPy to ensure high-quality datasets.
  • Engineered meaningful features with Featuretools and Scipy to optimize model performance.
  • Utilized SQL for data extraction, transformation, and analysis, integrating with PostgreSQL and MySQL

Until January 2024

Intern Data Scientist

National Center of Artificial Intelligence (NCAI) UET Peshawar

At NCAI, I had the opportunity to explore the power of cloud computing and machine learning in tackling big data challenges of national importance. My focus areas included

  • Developed and deployed predictive models for data-driven decision-making.
  • Built machine learning algorithms for supervised and unsupervised learning.
  • Cleaned, preprocessed, and engineered features to enhance model accuracy.
  • Utilized SQL for efficient data extraction, transformation, and analysis.
  • Conducted exploratory data analysis (EDA) to uncover patterns and insights.
  • Delivered predictive modeling and machine learning solutions



Education


2019-2023

Bachelor of Mathematics

Shaheed Benazir Bhutto University For Women Peshawar

Grade: First class distinction.

2017-2019

Higher Secondary School

Govt Frontier College For Women Peshawar, Pakistan

Grade: First class distinction.

Download CV

Projects

Data Analysis Projects

These projects are geared toward generating insights to inform decisions, often through statistical summaries and trend analysis. Common tools and technologies used in data analysis include Excel, SQL, Python (especially with libraries like Pandas and Matplotlib), R, Tableau, and Power BI, which together support a wide range of tasks from data manipulation to interactive visualization.

IMDb Cinematic Dashboard: 30,000+ Movies, 60 Years of Data

The dataset includes films from various countries, genres, and production houses, providing valuable insights into industry shifts and audience preferences. The project is powered by Tableau, SQL, and Python, with interactive dashboards and data-driven storytelling.

Online-Retail-Dataset (UCI-Machine-Learning-Repository) Power-BI Dashboard

This repository contains a Power BI dashboard for analyzing the Online Retail Dataset (UCI Machine Learning Repository). It provides key business insights using DAX measures and interactive visualizations. This main heading covers all aspects of customer behavior, purchase patterns, and monetary analysis.

Amazon Exploratory Data Analysis (EDA) - Baby Care Products

With over 4,000 product listings analyzed, the report highlights key metrics such as profit margin, discount percentage, product ratings, and price ranges. Baby products in the “toys & baby products” category show strong sales but also notable discounting, with some items yielding negative profit margins.


Publisher-Class-Analysis-with-R

This dashboard provides key insights into publisher performance by visualizing total revenue, price, copies sold, and average playtime across publisher classes. It highlights top revenue-generating publishers and helps assess engagement and sales strategies.

Telecom Egypt KPI Dashboard - Multi-Brand Excel Analysis

This project presents an advanced interactive Excel dashboard built to analyze and visualize key sales and operational KPIs for Telecom Egypt across seven major regional brands.

Projects

Data Science Projects

Data science projects often involve building machine learning models to uncover patterns and make predictions, beginning with feature engineering to transform raw data into meaningful inputs.

Apple Stock Price Prediction Using Machine Learning & AI (XGBoost-DeepLearning-FNN-RNN-LSTM)

This analysis involves preparing and scaling data, engineering features like moving averages and returns, and using a machine learning model to predict future prices. It includes evaluating model performance with metrics like MSE and R², and predicting future values based on estimated feature inputs.

Forecasting Attrition: A Data Analysis and Machine Learning (Random-ForestClassifier)

This analysis identifies and predicts customer churn using demographic data, transaction history, and account usage patterns. The dataset includes customer profiles, credit card behavior, and engagement metrics, supporting both descriptive analysis and predictive modeling to guide retention strategies.

Online Retail Dataset- UCI Machine Learning Repository (RFM analysis-Sales Forecasting)

The goal is to improve customer retention, optimize sales strategies, and enhance fraud detection. Key visual components include Customer Segmentation Charts, which identify distinct buyer personas based on purchasing behavior, and Sales Trends Graphs, illustrating seasonal and category-wise fluctuations to guide inventory and marketing strategies.


Optimizing Sales Prediction for Rohlik (Machine Learning-Weighted MAE Evaluation)

This project focuses on sales prediction using various machine learning techniques. The evaluation is centered on Weighted Mean Absolute Error (WMAE) along with other key metrics. The workflow includes data preparation, feature engineering, model training, evaluation, optimization, and deployment.

Jane Street Real-Time Market Data Forecasting (Machine Learning-EDA- Data Preparation)

Jane Street has been at the forefront of innovation in trading for decades, developing advanced machine learning models to support decision-making. These models assist in actively trading thousands of financial products daily across more than 200 global trading venues.

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More projects on Github

I love to solve business problems & uncover hidden data stories


GitHub

Contact

Contact Me

Below are the details to reach out to me!

Address

Peshawar, Pakistan

Contact Number

+(92) 3059549001

Email Address

rameenshahid60@gmail.com

rameenshahidprofessional@gmail.com

resume links

resume

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