Matthew Warkentin
About Candidate
Since 2014, Matthew has been working professionally in the fields he loves, software and data—culminating in him co-founding the Rubota corporation in 2017. Before that, he spent the past decade at Cornell University conducting scientific research specifically in statistical and biological physics. All in all, Matthew is an engaging, intense communicator with a passion for knowledge and understanding.
Location
Education
Work & Experience
Pioneered experimental techniques to exploit opportunities in the rapidly-evolving field of structural biology. Standardized and automated existing data collection and processing practices resulting in a greatly increased impact of the final product. Technologies: Linux, Python
Authored eight peer-reviewed studies in X-ray science, structural biology, and statistical mechanics. Developed novel analytical and visualization tools to investigate protein conformational motions. Managed teams running experiments at Cornell’s X-ray source and Argonne National Lab under extreme time pressure (typically 24 to 48 hours from start to finish). Built and maintained data pipelines to construct 3D models of macromolecules from 1000s of X-ray images. Technologies: Linux, Python
Produced prototypes and handled third-party integrations. Engaged with customers to understand their data and applications. Supported sales and marketing with demonstrations tailored to target customers. Technologies: Amazon Web Services (AWS), Python
Collected and integrated data from disparate sources into a unified model. Worked with the chief engineer to develop a platform data model. Integrated in-house and third-party entity analytics. Technologies: Machine Learning, Python
Developed end-to-end marketing data and analytics solution including scraping, fusion, predictive modeling, deployment, reporting, and evaluation. Completed studies and proofs of concept for company leadership and regularly advised at that level. Built a hybrid statistical/NLP model for the impact of online reviews. Created a qualitative analysis framework to develop coding schemes for survey responses. Developed a predictive model for an all-in cost of delivery to a customer using years of historical data. Collaborated on a product-pricing model for a prototype-phase eCommerce application. Technologies: Amazon Web Services (AWS), Matplotlib, Keras, TensorFlow, Scikit-learn, Pandas, SQLAlchemy, SQL, ECS, AWS, Python
