Resume

Experience

Use real world data (RWD) and machine learning/analytics to inform strategy within Global Medical & Scientific Affairs with a focus in oncology.

Provide guidance, development, and deployment of mid- to long-term goals of data products designed to reduce cost of monitoring clinical trials and detect anomalies in clinical trial data. Development and deployment involve using tools such as Python and machine learning libraries (e.g., PyTorch, scikit-learn).
Work with clients to solve problems related to clinical trial operations, which frequently involves developing machine learning methods that drive business impact (e.g., clinical trial complexity).
Communicate findings/recommendations to multiple company teams, develop training material for data science methods, and provide data science mentorship to colleagues.

Used deep learning (e.g., deep convolutional neural networks, recurrent neural networks) and other machine learning methods (e.g., random forests) to map functional MRI brain activation patterns.
Published peer-reviewed research; please see my Google Scholar page.
Managed/mentored teams of undergraduate/post-bac research assistants and junior graduate students.

Advanced statistical modeling, Amazon Web Services (AWS), Bash shell programming, computer vision, deep learning libraries (e.g., PyTorch, fastai), Docker, Git, Google Cloud Platform, Linux, machine learning, MATLAB, natural language processing, Python (including numpy, pandas, scikit-learn, etc.), recommender systems, SAS, SQL, Swift.