Michael Howard
About Michael Howard
Michael Howard is a Business Intelligence Analyst at project44 in Chicago, Illinois, with a background in data analysis and data science.
Current Position at project44
Michael Howard currently serves as a Business Intelligence Analyst at project44 in Chicago, Illinois, United States. His role involves leveraging data analytics and business intelligence tools to provide actionable insights for decision-making processes. He has developed a comprehensive data visualization dashboard using Power BI, aiming to enhance the effectiveness of decision-making within the organization.
Previous Experience at McMaster-Carr
From 2021 to 2022, Michael worked as a Data Analyst at McMaster-Carr in Chicago, Illinois. During his one-year tenure, he focused on extracting and analyzing data to provide insights that supported business operations. His role involved data cleaning, transformation, and visualization to drive efficiency and effectiveness in various projects.
Previous Experience at Gallagher
Michael worked at Gallagher as a Data Science Analyst from 2018 to 2021. Over his three years at Gallagher, he utilized advanced data analysis and machine learning techniques to analyze business data. His work contributed to optimizing various processes and identifying key performance indicators, thereby supporting strategic decision-making in the company.
Education and Academic Background
Michael Howard earned a Bachelor of Science in Statistics from the University of Illinois Urbana-Champaign, where he studied from 2016 to 2018. Before that, he achieved an Associate of Science in Mathematics from Waubonsee Community College, studying from 2014 to 2016. His academic background in statistics and mathematics provided a strong foundation for his career in data analysis and business intelligence.
Key Projects and Initiatives at project44
At project44, Michael led several impactful projects. He implemented machine learning models in Python to optimize supply chain logistics and conducted advanced statistical analysis using R to identify key performance indicators. He also led a team of analysts to integrate Azure cloud solutions for data storage and processing. His efforts in automating data extraction and transformation processes using SQL reduced manual workload by 30%. Additionally, he presented findings on data-driven strategies at an internal company conference and collaborated with cross-functional teams to enhance data governance and quality assurance protocols.