Led improvements to analytical and machine learning workflows on complex multimodal datasets, increasing the quality and reliability of downstream outputs. Built Python-based pipelines for cleaning, evaluation, transformation, and structured analysis on noisy real-world data. Worked with heterogeneous datasets from multiple sources to support model development, validation, and performance improvement. Investigated data quality issues, variation patterns, and distribution shifts that could affect analytical conclusions. Translated multimodal outputs into more structured and interpretable views for end users, improving usability and downstream review. Helped improve reproducibility, maintainability, and evaluation quality across technical workflows. Communicated findings clearly to both technical and non-technical stakeholders.

Improved data collection methods and data quality, increasing the usefulness of healthcare datasets for analysis and downstream modeling. Conducted exploratory and inferential analysis to identify patterns, trends, and segments that supported better decision-making. Built Python and SQL workflows for data cleaning, transformation, structured analysis, and feature-oriented preparation. Applied traditional analytics and predictive modeling to support more personalized care and operational improvements. Used patient behavior, medical history, and contextual data to support data-driven decisions around prioritization, follow-up, and operational resource allocation. Translated analytical findings into decision-support inputs for non-technical stakeholders, helping optimize staff allocation, adherence strategies, and follow-up interventions. Communicated findings clearly to decision-makers, helping convert raw data into actionable recommendations.