Kara Odum
About Kara Odum
Kara Odum is a data scientist with extensive experience in predictive modeling and demand forecasting. She holds dual degrees in Industrial Engineering and Mathematical Economics and has worked at Advance Auto Parts since 2017.
Work at Advance Auto Parts
Kara Odum has been employed at Advance Auto Parts since 2017, currently serving as a Data Scientist. In this role, she has developed demand forecasts and built assortments using KNN for clustering problems. She previously held the position of Regional Planner from 2016 to 2017, where she contributed to inventory management by analyzing model performance for distribution center inventory. Her programming skills in Python, R, Spark, and SQL have been instrumental in designing and implementing predictive models within the company.
Education and Expertise
Kara Odum holds dual undergraduate degrees: a Bachelor of Science in Industrial Engineering from Columbia University and a Bachelor of Arts in Mathematical Economics from Scripps College. She furthered her education by obtaining a Master of Science in Operations Research from North Carolina State University. During her master's program, she completed coursework in Non-Linear Algorithms, Stochastic Models, Dynamic Programming, and Simulation Modeling. Additionally, she pursued a minor in Statistics, completing courses in Time Series, SAS, Experimental Statistics, and Bayesian Statistics.
Background
Kara Odum began her professional journey at Scripps College, where she worked as an IT Intern from 2012 to 2013, followed by a role as a Math Tutor from 2013 to 2014. She also served as a Research Fellow for four months in 2013. In 2015, she gained experience as a Finance and Accounting Intern at Catholic Charities of the Archdiocese of Denver for two months. This diverse background in academia and internships laid the foundation for her career in data science and analytics.
Achievements
Throughout her career, Kara Odum has made significant contributions in the field of data science, particularly at Advance Auto Parts. She has successfully developed demand forecasts and implemented predictive models that enhance inventory management. Her analytical skills have been applied to improve model performance for distribution center inventory, showcasing her expertise in data analysis and operational efficiency.