Grant Weller
About Grant Weller
Grant Weller is the Senior Vice President of Data Science at BioIntelliSense, Inc., where he has worked since 2023. He has held various leadership roles in data science and engineering across multiple organizations, with a strong research interest in statistical methods for analyzing rare events.
Current Role at BioIntelliSense
Grant Weller serves as the Senior Vice President of Data Science at BioIntelliSense, Inc. since 2023. In this role, he oversees data science initiatives and contributes to the development of innovative solutions in health monitoring. His leadership is pivotal in advancing the company's mission to enhance patient care through data-driven insights.
Previous Experience at BioIntelliSense
Prior to his current position, Grant Weller worked at BioIntelliSense, Inc. as Vice President of Data Science from 2021 to 2023. During this time, he played a significant role in shaping the company's data strategies and implementing advanced analytics to improve health outcomes.
Professional Background at UnitedHealth Group
Grant Weller held multiple positions at UnitedHealth Group. He served as Vice President of Engineering from 2020 to 2021, where he developed enterprise-level innovation efforts and provided scientific guidance at the Research & Development division. He later transitioned to Vice President of Research from 2018 to 2019, further contributing to the organization’s research initiatives.
Educational Qualifications
Grant Weller earned a Ph.D. in Statistics from Colorado State University, where he also obtained a Master of Science in Statistics. His academic journey includes a Bachelor of Arts in Mathematics and Economics from Concordia College. He has also engaged in postdoctoral research and served as a visiting assistant professor, enhancing his expertise in statistical methodologies.
Research Interests and Contributions
Grant Weller has a research interest in statistical methods for analyzing rare, tail events, particularly through extreme value theory (EVT). His work encompasses a range of fields, including finance, climate science, environmental monitoring, and astrophysics. He has applied statistical and computational machine learning methodologies and conducted technical work in uncertainty estimation and Bayesian modeling.