Jun 2024 – Present · 2 yrs · Virginia, United States · Remote

• Engineered and refined core ML systems for prediction modeling using TensorFlow and PyTorch for deployment.
• Managed and streamlined production environments on AWS EC2 and SageMaker, integrating S3 storage and SQL databases.
• Hosted APIs with Flask and Django, resulting in minimal downtime, high throughput, and an 80% reduction in processing time and 50% decrease in latency.
• Interpreting and synthesizing user log data to measure compliance with user plans, biological and psychological effects of following the plan, etc. in partnership with top US and Canada Universities.
• Overseeing and coordinating three cross-functional teams on API management and product enhancements.

Jan 2024 – May 2024 · 4 mos · Virginia, United States · Remote

• Developed a predictive engine leveraging research data to analyze and map correlations between dietary patterns, interventions, exercise, and key health outcomes, including all-cause mortality and risk ratios for cancer, diabetes, stroke, and depression.
• Directed the machine learning team through all project stages, including data acquisition, feature engineering, model development, and deployment of an API service, ensuring seamless integration and scalability.

Sep 2023 – Dec 2023 · 3 mos · Virginia, United States · Remote

• Developing a platform at a startup that enables users to make lifestyle changes based on lifespan and disease risk predictions, leveraging over 50 years of scientific research data.
• Leading the Machine Learning team in building and maintaining the core ML API, which powers the platform’s prediction and recommendation engine.
• Driving innovation as a member of the core Data Science team by analyzing, integrating, and applying complex datasets to create actionable insights and solutions.