Design and develop machine learning models for time-series analysis, structured and unstructured data processing, and predictive analytics.
Create and implement data preprocessing pipelines for various data types, including time-series, structured (e.g., tabular), and unstructured (e.g., text, images).
Apply statistical techniques and feature engineering to optimize data representations for modeling.
Train, fine-tune, and evaluate machine learning and deep learning models, ensuring high accuracy, robustness, and scalability.
Stay up-to-date with the latest advancements in machine learning, deep learning, and time-series modeling, integrating innovative approaches into projects.
Work closely with data scientists, software engineers, product managers, and clinical experts to align on project requirements and ensure successful model deployment.
Prepare technical documentation, research papers, and presentations to communicate findings and results to both technical and non-technical audiences.