Fahad Azeem

As a Data Science graduate, I leverage machine learning, deep learning, and advanced analytics to transform complex data into actionable insights. My expertise in AI-driven modeling, predictive analytics, and robust data management enables me to develop intelligent solutions that enhance decision-making. I also have hands-on experience with MLOps and database management, driving innovation in today's digital landscape.

AI Developer - Punjab University College of Information and Technology

Final Year Project | ASD Support System — Prediction, Therapeutic Games & AI Assistant. Conducted research and development for early detection of Autism Spectrum Disorder (ASD) in young children by analyzing medical and behavioral features using machine learning and deep learning techniques.

• Developed two interactive therapeutic games based on research insights: Autism Counting Game — supports cognitive skill development and Autism Animal Matching Game — supports social skill development in autistic children
• Built ARIA (Autism Response & Interaction Assistant), an AI-powered support system designed for parents, therapists, teachers, and autistic children
• Implemented RAG Pipeline — citation-backed responses from uploaded ASD-related documents
• Integrated Real-time Emotion Detection — enables empathetic response adaptation
• Developed Multi-turn Memory — maintains conversational context across sessions
• Created Role-Specific Conversational Modes — tailored interactions per user type
• Built Personalized Routine Generator — custom daily routines for autistic users
• Implemented Social Skills Trainer — interactive skill-building module
• Developed Progress Tracking Dashboard — monitors user development over time
• Deployed using Groq API (Llama3) and HuggingFace embeddings for cost-efficient, high-performance inference

Data Science Engineer

FIFA World Cup 2026 Tournament Simulator. Built an end-to-end FIFA World Cup 2026 simulator featuring a 6-layer pipeline spanning data ingestion to ML-based match and bracket prediction.

• Developed 4 ML models from scratch in pure Python (no ML libraries): Gradient Boosted Trees Classifier — 83% accuracy, +32% improvement over baseline
• Trained Poisson & Negative Binomial Goal Models — via MLE with 10% MAE improvement
• Implemented 2-Layer MLP Neural Network — with manual backpropagation from scratch
• Designed a dynamic Elo Rating System with weighted K-factors and goal-difference adjustments for real-time team strength tracking
• Implemented a backtracking algorithm for valid FIFA bracket construction adhering to tournament constraints
• Incorporated Monte Carlo Simulations for match outcome variability and uncertainty modeling across tournament stages
• Engineered feature pipelines using FIFA Rankings, StatsBomb, and Transfermarkt data for robust team strength estimation
• Conducted WC 2022 backtesting and model validation to ensure predictive reliability and robustness

Bachelor's Degree - Data Science - Punjab University College of Information and Technology (2021 - 2025)