About
Highly motivated Data Scientist with an MSc in Machine Learning & AI, specializing in Natural Language Processing, deep learning, and cloud platforms (AWS/Azure). Proven ability to develop and optimize production-grade ML models, achieving 93% accuracy and 40% latency reduction. Certified in Generative AI (Google Cloud) and possessing 1+ years of hands-on Python experience, adept at leveraging data-driven insights to solve complex problems and drive business impact.
Work
Summary
Supported AI research by optimizing diagnostic accuracy and developing user-friendly data visualization tools for clinical applications.
Highlights
Improved clinical diagnostic accuracy by 12% through Principal Component Analysis (PCA) and strategic neural network architecture optimization.
Designed and deployed interactive visualization dashboards, facilitating data-driven decision-making and adopted by over 10 clinicians.
Summary
Contributed to data science initiatives by developing predictive models and automating analytical processes to improve business outcomes and operational efficiency.
Highlights
Reduced credit losses by 15% by implementing XGBoost with advanced feature engineering and SHAP value analysis, enhancing predictive accuracy.
Developed a robust churn prediction system leveraging ensemble learning, achieving an AUC-ROC of 0.89, and integrated it with Power BI for actionable insights.
Automated Exploratory Data Analysis (EDA) using Pandas Profiling, streamlining data preparation and reducing analysis time by 30%.
Summary
Focused on advancing Natural Language Processing capabilities through model engineering and pipeline optimization for enhanced performance.
Highlights
Engineered a DistilBERT-GRU model, achieving 93% sentiment analysis accuracy and an 8% improvement over previous benchmarks.
Optimized NLP pipeline latency by 40% through efficient implementation with TensorFlow Lite and ONNX runtime conversion, enhancing real-time performance.
Skills
Programming Languages
Python, SQL, Bash.
Machine Learning & Deep Learning
Supervised Learning, Unsupervised Learning, Ensemble Methods, Hyperparameter Tuning, Neural Networks.
Natural Language Processing (NLP)
BERT, Transformers, LLM Fine-Tuning, Text Embeddings, NER.
Cloud Platforms
AWS SageMaker, Azure ML, Docker, Kubernetes, CI/CD Pipelines.
Data Science Tools & Libraries
TensorFlow, PyTorch, scikit-learn, Pandas, NumPy, Power BI.