Kessinee C.
About Kessinee C.
Kessinee C. is a Senior Data Scientist at Benson Hill with a strong background in statistics and machine learning. She holds a PhD in Statistics, an MS in Applied Mathematics, and a BBA in Finance, and has experience in both academia and industry.
Work at Benson Hill
Kessinee C. currently serves as a Senior Data Scientist at Benson Hill, a role she has held since 2021. In this position, she applies her extensive knowledge in statistics and data science to address complex challenges within the organization. Her work focuses on leveraging data-driven insights to support decision-making processes and enhance operational efficiency.
Previous Experience at Kansas State University
Kessinee C. worked at Kansas State University in two capacities. From 2013 to 2016, she served as a Graduate Teaching Assistant, where she supported the educational mission of the university. Subsequently, from 2017 to 2018, she was a Graduate Research Assistant, engaging in research activities that contributed to her academic development and expertise in statistics.
Experience at Pig Improvement Company
Kessinee C. was employed as a Product Analytics Specialist at Pig Improvement Company (PIC) in Hendersonville, TN, from 2019 to 2021. In this role, she focused on product analytics, utilizing her statistical skills to analyze data and provide insights that informed product development and strategy.
Education and Expertise
Kessinee C. holds a Doctor of Philosophy (PhD) in Statistics from Kansas State University, which she completed from 2012 to 2019. She also earned a Master of Science (MS) in Applied Mathematics from California State University, Long Beach, from 2010 to 2012. Additionally, she has a Bachelor of Business Administration (BBA) in Finance from Mahidol University, obtained from 2003 to 2007. Her educational background equips her with a strong foundation in both quantitative analysis and financial principles.
Statistical Methodologies and Research Experience
Kessinee C. possesses a strong background in classical and Bayesian statistics, with a particular focus on causal inference and hierarchical Bayesian methodologies. She has research experience in graphical models and machine learning, which includes interfacing with designed experiments and observational studies. Her expertise enables her to tackle big-data challenges and apply interdisciplinary approaches in her work.