• Improve diagnostic prediction accuracy by 25% by developing and applying machine learning algorithms to analyze medical imaging data from electronic health records (EHRs)• Reduced manual reporting by 40% through automated workflows with Python and SQL, leading to more accurate and consistent reporting and a 12% increase in data-driven project success rates• Collaborate with key stakeholders to understand business needs, pulling data from multiple sources using SQL and creating customized analytics that helped reduce decision-making time by 20%• Contribute to end-to-end analytics projects, from data gathering and requirements specification to processing and delivering actionable insights• Identify data discrepancies and implemented cleaning processes, increasing data accuracy by 30%, while establishing validation checks and automated quality controls to ensure high data integrity for analysis and reporting

• Conducted data cleaning and validation across multiple sources, reducing errors by 18% and establishing documentation and best practices for ongoing data quality checks• Delivered clear, concise reports and visualizations using Python and Power BI that highlighted key business trends, leading to a 15% improvement in operational performance by identifying areas for process improvement• Contributed to the development of data-driven insights for new treatment plans and processes, leading to a 22% improvement in the accuracy of patient risk assessments• Assisted in developing and evaluating machine learning algorithms for medical imaging data analysis, enhancing model performance by 15%

• Managed and analyzed a large image database to provide feedback to baseball players• Developed equations to find the lowest minimum mean square error on scatterplots to provide accurate predictions