Larry Baeder
About Larry Baeder
Larry Baeder is a Data Scientist with a Master's degree in Statistics from Texas A&M University and a Bachelor's degree in Computer Science from the University of Wisconsin-Madison. He currently works at Milliman, where he leads a team of data scientists on projects related to telematics and insurance pricing.
Current Role as Data Scientist
Larry Baeder currently holds the position of Data Scientist at Milliman, where he has been employed since 2018. His work is based in the San Francisco Bay Area. In this role, he leads a team of data scientists dedicated to integrating data science methodologies into the insurance sector. His focus includes various projects that leverage data analysis to enhance insurance products and services.
Education and Expertise
Larry Baeder earned a Master's degree in Statistics from Texas A&M University, where he studied from 2015 to 2018. Prior to this, he completed a Bachelor's degree in Computer Science at the University of Wisconsin-Madison from 2000 to 2005. His educational background provides a strong foundation for his work in data science, particularly in statistical analysis and computational methods.
Technical Skills and Competencies
Larry Baeder possesses a range of technical competencies that are essential for his role as a Data Scientist. He is proficient in programming languages and tools such as R, Python, PySpark, Databricks, and SAS. These skills enable him to effectively analyze data and develop models that support decision-making within the insurance industry.
Experience at Milliman
Larry Baeder has a history with Milliman that dates back to 2018 when he started as a Data Science Intern for a duration of two months in the San Francisco Bay Area. His internship provided him with initial exposure to the company's operations and laid the groundwork for his subsequent full-time role as a Data Scientist, where he has continued to develop his expertise in the field.
Project Involvement
In his current role, Larry Baeder works on various projects that include telematics, homeowners pricing, flood insurance, interpretable machine learning, and commercial auto. These projects reflect his commitment to applying data science techniques to real-world challenges in the insurance industry, contributing to the development of innovative solutions.