David is a driven, team-orientated Data Scientist with experience creating machine learning and cloud-native data solutions for Deloitte’s Healthcare clients. He has hands-on experience in data transformation, feature engineering, data visualization, and machine learning.During his time at Deloitte, David has:- Created an automated model performance assessment tool in Python that was leveraged to compare the performance of multiple versions of over 23 clinical ML models, ensuring performance improvement between production model deployments- Developed, tuned, and selected a suite of six classification models that successfully predicted onset and progression from chronic kidney disease to end-stage renal disease from health insurance claims data with >94% accuracy in less than two weeks using random forest and gradient boosting techniques- Designed and implemented a serverless, no-code data extraction API, eliminating the need for SQL programming to generate extracts from cloud-based data warehouses. This solution has been developed and tested in GCP/BigQuery and been deployed at a client in Azure/Snowflake