CJ Jenkins is a self-driven PhD and data science lead focused on credit risk modeling, integrating data-driven decisions into products to reduce risk and enhance the customer experience. With a strong background in statistical analysis, CJ works fluently across SQL, R, and Python, and pairs deep quantitative skills with clear communication, a combination that has led to invitations to present research and represent universities at senior academic conferences.

CJ’s route into data science ran through academia: a doctorate rooted in evolutionary biology and genomics, with statistical machine learning foundations built on tools such as generalized linear models and population dynamics modeling. That research produced a number of well-cited peer-reviewed papers and a textbook currently used in academic curricula, and it instilled a habit of turning open questions into testable answers.

Since moving from postdoc to industry, CJ has added practical engineering skills to the scientific toolkit, including deploying models with Docker and translating academic output into production impact. Today CJ applies that blend of scientific rigor and commercial focus to credit risk problems, where models need to be accurate and actionable at the same time.