• Designed and deployed end-to-end machine learning solutions using Python, TensorFlow, and Scikit-learn to predict patient risk, hospital readmissions, and care gaps, improving predictive accuracy by 32% and supporting proactive care interventions across large healthcare populations.

• Built scalable data ingestion and preprocessing pipelines using PySpark, SQL, and AWS S3 to process multi-terabyte clinical, claims, and behavioral datasets, reducing data preparation time by 40% while ensuring high data quality and regulatory compliance.

• Developed deep learning models including LSTM and transformer-based architectures for longitudinal patient data analysis, enabling early risk detection and reducing avoidable hospitalizations by 18% across monitored cohorts.

• Implemented NLP pipelines using Python and TensorFlow to extract structured insights from unstructured clinical notes, improving documentation analysis accuracy by 29% and enhancing downstream clinical decision support systems.

• Deployed machine learning models using Docker, Kubernetes, and AWS SageMaker, enabling automated scaling, version control, and rollback strategies that achieved 99.9% system uptime in production environments.

• Collaborated with data engineering, clinical analytics, and business stakeholders to translate healthcare requirements into AI-driven solutions, accelerating model adoption and reducing decision latency by 35% across care management workflows.

• Established MLOps workflows with CI/CD pipelines for continuous training, testing, and monitoring of models, reducing model drift incidents by 27% and ensuring consistent performance across production releases.

• Conducted advanced feature engineering and hyperparameter optimization using grid search and Bayesian techniques, improving model precision and recall metrics by an average of 21% across multiple healthcare use cases.

• Developed machine learning models using Python, Scikit-learn, and XGBoost to support healthcare analytics initiatives, including cost prediction, utilization forecasting, and population health management, improving forecast accuracy by 26%.

• Engineered robust ETL pipelines using PySpark, SQL, and cloud storage systems to integrate data from EHRs, claims systems, and external data sources, reducing manual data handling efforts by 45%.

• Implemented deep learning models using TensorFlow and Keras for anomaly detection in healthcare transactions, reducing fraudulent or erroneous claim processing by 22% across client platforms.

• Built NLP-based text classification and entity recognition solutions to analyze clinical documentation and physician notes, improving information extraction efficiency by 31% and supporting clinical quality reporting.

• Deployed models into production using Dockerized microservices and REST APIs, enabling seamless integration with client applications and improving inference response times by 38%.

• Collaborated with cross-functional teams including data scientists, healthcare SMEs, and QA engineers to ensure model outputs aligned with clinical and regulatory requirements, reducing post-deployment defects by 25%.

• Optimized model performance through feature selection, dimensionality reduction, and tuning strategies, achieving consistent improvements in AUC and F1 scores across multiple projects.

• Implemented automated model validation and monitoring frameworks to track performance metrics, data drift, and bias, improving long-term model stability and compliance readiness.

• Created technical documentation and knowledge transfer materials to support client onboarding and internal teams, accelerating deployment cycles and reducing handover time by 30%.