ML Engineer with 2+ years in Predictive Analytics & Recommendation Systems.
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Data Science graduate (GPA 3.48/4.0) from Alexandria University specializing in Machine Learning, Predictive Analytics, and Recommendation Systems. Proficient in Python, LightGBM, TensorFlow, PyTorch, FastAPI, and SQL, with hands-on experience across the full ML lifecycle from data collection and feature engineering to model training, evaluation, and API deployment. Adept at working with structured and unstructured data, applying statistical analysis, and delivering data-driven solutions across multiple domains.
Faculty of Computer and Data Science, Alexandria University
B.Sc. · Data Science
N/A – June 30, 2026
Freelance / Independent Projects
Machine Learning Engineer
January 1, 2024 – Present
India
Abu Qir Electricity Company
Network Engineering Intern
January 1, 2023 – December 31, 2023
India
Weather Impact on Urban Traffic
June 25, 2026 – Present
Analyzed 100,000+ traffic and weather records using correlation analysis and Monte Carlo simulation (10,000 iterations) to quantify how rainfall, temperature, and visibility affect congestion across 4 city districts.
IoT Smart Monitoring System
June 25, 2026 – Present
Wired and programmed a 4-sensor monitoring system (temperature, humidity, motion, light) with automated alert logic; anomaly detection flagged readings deviating more than 2 standard deviations from a 60-second rolling baseline, reducing false-positive alerts.
Customer Churn Prediction
June 25, 2026 – Present
Built a churn classifier on 7,000+ telecom customer records; applied SMOTE oversampling to correct a 4:1 class imbalance, improving minority-class recall by 18 percentage points. Reduced feature space from 21 to 13 variables via Recursive Feature Elimination (RFE), cutting prediction time by 30% while maintaining accuracy.
Property Hub - AI-Powered Real Estate Platform
June 25, 2026 – Present
Scraped 26,610 property listings from Aqarmap and Dubizzle across 15+ Egyptian cities, then applied a 6-stage pipeline: deduplication, null imputation, categorical encoding, TF-IDF vectorization, KMeans clustering, and log-price transformation. Extracted 30+ engineered features per listing - including price-per-sqm ratios, geospatial cluster labels, and interaction frequency scores to feed both the price prediction and recommendation models. Fitted a LightGBM Regressor with RandomizedSearchCV over 50+ parameter combinations using group-based splitting to prevent data leakage, selecting the final model on generalization to unseen listings. Addressed the cold-start problem by combining content-based filtering, collaborative filtering, and learning-to-rank scoring across 5 interaction types: views, clicks, favorites, contacts, and searches. Exposed 12+ ML-powered REST API endpoints via FastAPI with JWT authentication, serving 26,000+ listing records. Led ML development within a 5-member team over a 9-month timeline.
Student Performance Prediction
June 25, 2026 – Present
Implemented 3 classifiers (Logistic Regression, Random Forest, XGBoost) on 1,000+ student records, raising F1-score by 12% through feature selection that removed 8 low-importance variables. Validated the final model using 5-fold cross-validation, confirming stable accuracy across all folds.
Machine Learning
National Telecommunications Institute (NTI)
June 1, 2026 – Present
IoT, Artificial Intelligence & Machine Learning
MAMI
June 1, 2026 – Present
Deep Learning
NVIDIA Deep Learning Institute
June 1, 2026 – Present
Data Science
Cisco Networking Academy
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic background in Data Science and diverse project portfolio (traffic analysis, IoT, real estate, churn prediction, student performance) indicate a strong interest and adaptability across various domains. The freelance work demonstrates proactivity and a results-oriented mindset. The role as an ML Engineer aligns well with the target role, showing a clear career path. The breadth of skills listed, from programming to databases and visualization, suggests a well-rounded technical individual.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through various project descriptions, tackling issues like class imbalance, data leakage, and cold-start. The ability to lead ML development in a team setting (Property Hub project) indicates potential for collaboration. The detailed project descriptions suggest good communication of technical work. The freelance experience shows initiative and self-direction.