• Work with large datasets from various sources such as sensors, logs, and business systems; pre-process and clean data using Pandas, NumPy, and SQL.• Develop effective features to enhance the performance of models, applying techniques like dimensionality reduction (PCA, LDA), normalization, and feature scaling.• Evaluate model performance using metrics like accuracy, precision, recall, F1 score, and AUC-ROC; optimize models with Grid Search CV and Randomized Search CV.• Develop and deploy predictive models for forecasting production performance, failure rates, and energy consumption using time series analysis and advanced regression models.• Implement real-time machine learning models and pipelines using Apache Kafka, Apache Spark, and AWS Kinesis for data streaming and event-driven architecture.

• Optimize Machine Learning Models for Speed and Efficiency using model quantization, pruning, and advanced techniques like TensorRT and ONNX to improve inference speed on production systems.• Design and Implement Custom APIs to provide model access and predictions to other business applications, leveraging FastAPI or Flask for efficient API development and integration.• Ensure Data Security and Compliance by implementing encryption protocols and adhering to HIPAA regulations during data handling, processing, and storage throughout the machine learning lifecycle.• Build and Train Machine Learning Models to optimize healthcare operations, including cost prediction, patient triage, and medical risk analysis, using scikit-learn, XGBoost, and LightGBM.

• Develop and Implement Predictive Models using Random Forests, Gradient Boosting, and XGBoost to forecast customer creditworthiness and improve lending decision-making.• Implement Model Interpretability Techniques like SHAP and LIME to ensure the transparency of complex models and their applicability in high-stakes financial decision-making.• Contribute to the Development of Machine Learning APIs using Flask or FastAPI to integrate model predictions into business applications for risk assessment and customer engagement.• Leverage Edge Computing technologies such as NVIDIA Jetson or Intel Movidius to deploy lightweight machine learning models on client devices, enhancing real-time decision-making in customer-facing applications