S.m. Saad

A highly motivated Physicist and Data Scientist with a strong focus on software engineering, machine learning, and IoT development. With consistent demonstration of tackling complex analytical problems, I build end-to-end data pipelines, predictive models, and intuitive web platforms. Whether deploying hybrid machine learning models to predict financial risk or developing full-stack solutions for hardware integration, I thrive at the intersection of data, software, and scientific computing to deliver actionable insights and scalable solutions.

Data Analyst & AI Intern - RESOLVE South, SUPARCO Complex

(2025-06 - 2025-08)

• Solar Panel Detection & Change Detection AI Model: Developed a YOLOv8-based detection and monitoring pipeline of solar panels from satellite imagery, enabling model comparison for accuracy and renewable-energy monitoring insights.

Data & Machine Learning Researcher - NED University

(2024-12 - 2025-06)

• Bankruptcy Risk Prediction Using Hybrid ML Models: Designed a Hybrid Dynamic Panel Probit + ANN (11-neuron single hidden layer, cross-entropy loss, k-fold CV) model using financial, macroeconomic, and market variables.
• Flood Damage Prediction Model: Created a two-stage classification pipeline (inundation depth + material type) using optimized Random Forest achieving 95% accuracy for minor damage and 83% for severe damage.

Machine Learning Intern - Pinnacle Full-Stack Interns

(2024-06 - 2024-07)

• Email Spam Detection System: Applied text preprocessing and implemented decision tree classification models for precise spam detection.
• Heart Disease Classification: Developed an ML classification pipeline tailored for healthcare and diagnostic analytics.

Data Analyst Intern - Placement Dost

(2024-04 - 2024-06)

• Flight Activity Dashboard: Built comprehensive Power BI dashboards to analyze booking trends, CLV segmentation, geographic distribution, and loyalty impact.

BS - Physics - NED University of Engineering & Technology (2022-10 - 2026-12)

Applied Data Science with Machine Learning - Data Science - UK Tech Hub (2023-10 - 2024-11)