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Machine Learning Lead of a team creating and deploying models for First Party Fraud Detection at Capital One

Led and constructed all efforts in personalization at Goldbelly including search and email recommendations.Devised and created the automated Airflow process to pre-train a personalization model and integrated it with the existing daily search infrastructure to enable personalized search. In addition, designed, ran, and analyzed the A/B test and subsequent rollout of personalized search, which added 5% to logged-in conversions. Productionalized a product embedding (BERT model embedding) into our current search training and inference pipelines. Led A/B testing for several model improvements, totaling an average of 8-10% conversion uplift, and created detailed reports to share with both the larger tech organization and the finance team.

Co-founded the Therapies AI team - a new data science team that focuses on tools and knowledge extraction to increase the capabilities of the Therapies business unit.Created and deployed two systems for Therapies AI: a ranking system to help the business development team recruit more clinical trials (helping Tempus gain contracts worth more than $1 million) and a model helps the Therapies team identify potential patients for a clinical trial from a patient's unstructured EMR.

Developed a number of deep learning models to help match patients to clinical trials in real time. Deployed an ensemble model (Prototypical Network and SVM) to our production system. Collaborated with nurses and physicians to help with workflow adoption of the system and gain insights to system improvements.Built data pipelines that power the clinical trial matching system and created daily testing methods to alert the data science team of potential data or model issues.

My research focused on implementing deep learning on new and challenging real-world problems as well as creating novel algorithmic techniques to make data and model discoveries via deep neural networks.