Who is Andrew Ford from Jeopardy? Meet the Data Scientist and Applied Mathematics Ph.D. Behind the Numbers

Andrew Ford is a data scientist based in Madison, Wisconsin, whose professional career combines advanced mathematics, computational modeling, and real-world data analysis. With a Ph.D. in Applied Mathematics and experience spanning government, healthcare technology, and pharmaceutical data management, Ford has built a career focused on solving complex problems through quantitative methods.

His background includes academic research in molecular dynamics, software engineering in large healthcare systems, and statistical work within the U.S. government. Appearing as a contestant on Jeopardy introduces viewers to a professional whose work revolves around mathematics, programming, and advanced analytics.

Academic Background and Doctoral Research

Andrew Ford earned a Ph.D. in Applied Mathematics from the University of North Carolina at Chapel Hill, completing the program in May 2022. His doctoral research focused on molecular dynamics modeling of human lung mucus, an area that blends mathematics, physics, and biology to better understand respiratory systems.

His dissertation examined the heterogeneous structure and rheology of lung mucus, which refers to how the material flows and behaves under stress. Understanding these properties is important in studying respiratory health and diseases affecting the lungs. Ford used advanced computational methods to simulate the molecular structure and movement of mucus, helping researchers analyze its behavior at a microscopic level.

During his graduate work, he developed predictive molecular dynamics models using stochastic differential equations. These models were implemented with scientific computing tools such as LAMMPS and VMD, both widely used in physics and materials science simulations. His research required running large-scale simulations, analyzing simulation outputs, and applying statistical techniques and image analysis to interpret the results.

Ford also focused on improving the computational efficiency of his simulations. He implemented optimizations through multithreading and improved memory management, which allowed simulations to run faster and handle larger data sets. This combination of mathematical theory and practical computing formed the foundation of his later career in data science.

Professional Work as a Data Scientist

Ford’s career includes several roles where he applied mathematical and statistical expertise to large and complex data systems. From December 2024 to September 2025, he worked as a Mathematical Statistician, also known as a Data Scientist, at the Internal Revenue Service.

Within the IRS Research, Applied Analytics, and Statistics division, he worked on projects aimed at improving data quality within the agency’s Compliance Data Warehouse. His work involved analyzing large tax data sets and evaluating programs such as clean energy tax credits.

He also helped develop frameworks for managing internal data systems and ensuring the reliability of large government databases. These types of projects require careful statistical analysis, database management, and the ability to work with sensitive and highly structured data.

Since October 2025, Ford has worked as a Data Management Consultant at Object Pharma, Inc. In this role, he focuses on organizing and digitizing decades of laboratory records that previously existed only in paper form. By converting these records into searchable digital systems, he helps improve accessibility and efficiency within the company’s research processes.

He also develops interactive Excel-based tools used for laboratory scheduling and data management. These tools allow teams to better organize experiments, track resources, and coordinate research workflows.

Software Development and Technical Experience

Before transitioning fully into data science roles, Ford gained significant experience as a software developer. From June 2022 to September 2023, he worked at Epic Systems in Verona, Wisconsin, one of the largest developers of electronic health record software in the United States.

At Epic, Ford worked on the Tapestry platform, which is used by healthcare organizations to manage health insurance claims. His responsibilities included building data pipelines that allowed information to move efficiently between systems within Epic’s software ecosystem.

He also implemented validation tools designed to reduce data entry errors and improve the accuracy of health insurance claims processing. The role required close collaboration with healthcare clients to refine workflows and ensure that the software systems met the needs of hospitals and insurance providers.

Earlier technical experience included a software development internship at Open Systems International, where he built tools used to reproduce power grid systems for debugging. These tools allowed engineers to recreate real-world power grid conditions in order to diagnose technical issues.

Teaching and Academic Instruction

While completing his doctoral degree, Ford spent nearly six years working as both a Graduate Research Assistant and Graduate Teaching Assistant at UNC Chapel Hill. In addition to his research responsibilities, he played an active role in teaching undergraduate mathematics courses.

Ford served as the instructor of record for several mathematics classes and also assisted with courses including Calculus I, Calculus II, Calculus III, Differential Equations, and Linear Algebra. Teaching these foundational subjects required both strong subject knowledge and the ability to explain complex mathematical concepts clearly.

His work as an instructor earned recognition within the department. He was nominated for the J. Burton Linker Award, which recognizes excellence in undergraduate instruction at UNC Chapel Hill. The nomination reflected his contributions to teaching and mentoring students studying mathematics.

Earlier in his career, Ford also worked as a mathematics tutor with Varsity Tutors and served as an undergraduate teaching assistant at the University of Minnesota.

Education and Academic Foundations

Ford began his academic journey at the University of Minnesota Twin Cities, where he earned a Bachelor of Science in Mathematics and Statistics between 2011 and 2015. His undergraduate studies provided the foundation in statistical theory, mathematical modeling, and programming that later shaped his research career.

During his time at the university, he also worked as an Undergraduate Teaching Assistant, helping support mathematics instruction for other students.

He later continued his studies at the University of North Carolina at Chapel Hill, where he pursued graduate education in applied mathematics. His doctoral research combined mathematics, physics modeling, and computational science, preparing him for technical roles in data science and software engineering.

Technical Skills and Certifications

Andrew Ford’s professional skill set includes a wide range of programming languages and analytical tools used in modern data science. His technical expertise includes Python, SQL, R, and C++, along with experience using Git for version control and various tools for statistical analysis and data visualization.

He also has experience in software architecture, cluster analysis, quantitative modeling, and object-oriented programming. His background in computational physics and mathematical modeling gives him a strong foundation in designing simulations and analytical models.

Ford has also completed professional certifications in machine learning through programs offered by DeepLearning.AI and Stanford University. These certifications cover supervised machine learning techniques such as regression and classification, advanced learning algorithms, and unsupervised learning approaches including recommender systems and reinforcement learning.

In addition to his technical work, he maintains an active professional presence on LinkedIn, where he participates in discussions related to mathematics, data science, and research.

Name: Andrew Ford
Location and Residence: Madison, Wisconsin, United States
Profession and Jobs: Data Scientist; Data Management Consultant at Object Pharma, Inc. (Oct 2025–Present); Mathematical Statistician (Data Scientist) at the Internal Revenue Service (Dec 2024–Sep 2025); Software Developer at Epic (Jun 2022–Sep 2023); Graduate Research Assistant and Mathematical Modeler at the University of North Carolina at Chapel Hill (Aug 2018–May 2022); Graduate Teaching Assistant at UNC Chapel Hill (Aug 2016–May 2022); Mathematics Tutor at Varsity Tutors (Sep 2015–Aug 2016); IAH Analyst at Doctors Making Housecalls (Apr 2016–Jul 2016); Undergraduate Teaching Assistant at the University of Minnesota (Jan 2014–May 2015); Software Development Intern at Open Systems International (May 2014–Aug 2014)
Gender and Sex: Male
Age and Date of Birth:
Nationality and Ethnicity: American
Education and School: Ph.D. in Applied Mathematics, University of North Carolina at Chapel Hill (Aug 2016–May 2022); Dissertation: Molecular Dynamics Modeling of Heterogeneous Structure and Rheology of Human Lung Mucus. Bachelor of Science in Mathematics and Statistics, University of Minnesota Twin Cities (2011–2015)
Relationships (Married/Dating/Sexuality) and Family:
Biography and More Details: Data scientist with expertise in mathematics, statistics, and software development. Experienced in Python, SQL, R, C++, statistical analysis, data visualization, and data pipeline development. Developed predictive molecular dynamics models studying lung mucus and respiratory disorder treatments during Ph.D. research. Built data pipelines and validation tools at Epic for health insurance claims systems. Worked at the IRS improving data quality in the Compliance Data Warehouse and analyzing clean energy tax credit usage. Currently working as a data management consultant digitizing and organizing pharmaceutical data records and building Excel-based scheduling tools. Taught mathematics courses including Calculus I–III, Differential Equations, and Linear Algebra as instructor of record and teaching assistant. Nominated for the J. Burton Linker Award for undergraduate teaching. Certifications include Supervised Machine Learning: Regression and Classification, Advanced Learning Algorithms, and Unsupervised Learning, Recommenders, and Reinforcement Learning through DeepLearning.AI and Stanford University. Skills include programming languages, quantitative analytics, cluster analysis, object-oriented programming, software design, and physics modeling. LinkedIn profile: https://www.linkedin.com/in/andrew-ford-99092663