Dan Pfeiffer
About Dan Pfeiffer
Dan Pfeiffer is a Data Scientist with a strong background in mechanical engineering and software engineering. He has developed significant applications and machine learning models that enhance project estimation and energy usage analysis.
Work at Redaptive
Dan Pfeiffer currently serves as a Data Scientist at Redaptive, Inc., a position he has held since 2022. In this role, he utilizes his expertise in data analysis and machine learning to support the company's initiatives in energy efficiency and sustainability. Prior to this, he worked as a Technical Solutions Engineer at Redaptive from 2020 to 2021, where he contributed to technical projects and solutions aimed at optimizing energy usage.
Education and Expertise
Dan Pfeiffer earned a Bachelor of Science in Mechanical Engineering from the University of Wisconsin-Madison, completing his studies from 2007 to 2011. He furthered his education by obtaining a Master of Science in Computer Software Engineering from DePaul University, where he studied from 2014 to 2018. His educational background provides a strong foundation in both engineering principles and software development.
Background
Dan Pfeiffer has a diverse professional background in data science and engineering. He began his career as a Roadcrew Operator at Mandli Communications in 2010 for four months. He then joined Johnson Controls, where he held several positions from 2011 to 2020, including Systems Technician II, Lead Systems Specialist, and Systems Engineer. His experience in these roles contributed to his technical skills and understanding of systems integration.
Achievements
Dan Pfeiffer has developed significant applications and tools in his career. He created an application that generated over $3 billion in cashflow positive leads by estimating thousands of potential solar projects quickly. He also spearheaded the development of a machine learning model that automated project estimation, significantly reducing the time required for this process. Additionally, he engineered a data enrichment tool that disaggregates energy usage and approximates HVAC equipment capacity, which serves as a foundation for various machine learning applications.