Machine Learning Engineer and Data Scientist
Sara is a Machine Learning Engineer and Data Scientist with hands-on experience in building end-to-end ML pipelines, recommendation systems, and deep learning models. She is proficient in Python, SQL, Scikit-learn, TensorFlow, and PyTorch, with expertise in feature engineering, model evaluation, and data visualization. Sara is passionate about AI applications, including Recommendation Systems, Anomaly Detection, and Predictive Analytics.
Expertise in building and deploying end-to-end machine learning pipelines, deep learning models, and advanced model evaluation techniques.
Skilled in data analysis, transforming raw datasets into actionable business insights, and creating interactive data visualizations.
Specialized in designing and implementing hybrid Learning-to-Rank recommendation systems for personalized user experiences.
Strong foundational knowledge in AI, including NLP, LLM fundamentals, Computer Vision, and complete AI workflow management.
Designed a hybrid Learning-to-Rank recommendation system for personalized community suggestions on the Mentora platform. Engineered behavioral, social, and mentorship-based features; built candidate generation and LightGBM LambdaRank ranking pipelines.
Impact: Achieved 96.5% HitRate@5 and 0.5665 NDCG@5, demonstrating strong personalization and ranking quality.
Built an end-to-end ML pipeline with feature engineering and EDA. Designed an interactive Tableau dashboard to visualize HR insights and key attrition drivers.
Impact: Achieved 82% classification accuracy (LightGBM) and 84% regression accuracy (Gradient Boosting).
Developed an anomaly detection system classifying network traffic as normal/malicious. Performed preprocessing, EDA (heatmaps, boxplots, histograms), and exported deployment-ready models via Pickle.
Impact: Achieved 98% accuracy (Random Forest) and 94.5% (Neural Networks).
Completed hands-on training in NLP, Computer Vision, ML, and Deep Learning using IBM Watson.
Studied AI/ML/DL fundamentals, CNNs, RNNs, Transformers, and foundation model architectures through Huawei HCIA AI.
Transformed raw datasets into actionable business insights and built interactive dashboards as a Data Analysis Intern.
Designed and implemented a hybrid Learning-to-Rank recommendation system, achieving 96.5% HitRate@5.
Completed the Google Data Analysis program, gaining proficiency in Excel, SQL, Python, Tableau, and Power BI.