Jay Kumar Email & Phone Number

Plano, TX, US

Houston, Texas, United States

• 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.

Texas, United States

• 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.

Bengaluru, Karnataka, India

• 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