Paul is primarily interested in studying explainable machine learning (ML), digital transformation, and data science operations. He works on research that explores how stakeholders within organizations can use machine learning to make better decisions. In particular, he is studying how domain experts use model explanations to reconcile their subject-area expertise with the predictions of machine learning systems. He also studies how firms are using data science and machine learning to transform their business operations.
Paul Hamilton
Doctoral Student
Doctoral Student
Paul studies the economic complements needed for firms to realize productivity gains from machine learning and artificial intelligence. These complements include data, human capital & skills, organizational processes, and business models.
Paul Hamilton is a doctoral student in the Technology and Operations Management program at Harvard Business School (HBS). He is primarily interested in studying explainable machine learning (ML), digital transformation, and data science operations. He works on research that explores how stakeholders within organizations can use machine learning to make better decisions. In particular, he is studying how domain experts use model explanations to reconcile their subject-area expertise with the predictions of machine learning systems. He also studies how firms are using data science and machine learning to transform their business operations.
He is a member of Hima Lakkaraju's Trustworthy AI Lab and Iavor Bojinov’s Data Science Operations Lab, both a part of Harvard’s D^3 Initiative. Prior to starting the doctoral program, Paul served as a resarch associate at HBS. Before that he worked in economic consulting. Paul received his B.S. in economics from the University of Michigan.
Paul Hamilton is a doctoral student in the Technology and Operations Management program at Harvard Business School (HBS). He is primarily interested in studying explainable machine learning (ML), digital transformation, and data science operations. He works on research that explores how stakeholders within organizations can use machine learning to make better decisions. In particular, he is studying how domain experts use model explanations to reconcile their subject-area expertise with the predictions of machine learning systems. He also studies how firms are using data science and machine learning to transform their business operations.
He is a member of Hima Lakkaraju's Trustworthy AI Lab and Iavor Bojinov’s Data Science Operations Lab, both a part of Harvard’s D^3 Initiative. Prior to starting the doctoral program, Paul served as a resarch associate at HBS. Before that he worked in economic consulting. Paul received his B.S. in economics from the University of Michigan.
Contact Information
Paul studies the economic complements needed for firms to realize productivity gains from machine learning and artificial intelligence. These complements include data, human capital & skills, organizational processes, and business models.
Paul Hamilton is a doctoral student in the Technology and Operations Management program at Harvard Business School (HBS). He is primarily interested in studying explainable machine learning (ML), digital transformation, and data science operations. He works on research that explores how stakeholders within organizations can use machine learning to make better decisions. In particular, he is studying how domain experts use model explanations to reconcile their subject-area expertise with the predictions of machine learning systems. He also studies how firms are using data science and machine learning to transform their business operations.
He is a member of Hima Lakkaraju's Trustworthy AI Lab and Iavor Bojinov’s Data Science Operations Lab, both a part of Harvard’s D^3 Initiative. Prior to starting the doctoral program, Paul served as a resarch associate at HBS. Before that he worked in economic consulting. Paul received his B.S. in economics from the University of Michigan.
Paul Hamilton is a doctoral student in the Technology and Operations Management program at Harvard Business School (HBS). He is primarily interested in studying explainable machine learning (ML), digital transformation, and data science operations. He works on research that explores how stakeholders within organizations can use machine learning to make better decisions. In particular, he is studying how domain experts use model explanations to reconcile their subject-area expertise with the predictions of machine learning systems. He also studies how firms are using data science and machine learning to transform their business operations.
He is a member of Hima Lakkaraju's Trustworthy AI Lab and Iavor Bojinov’s Data Science Operations Lab, both a part of Harvard’s D^3 Initiative. Prior to starting the doctoral program, Paul served as a resarch associate at HBS. Before that he worked in economic consulting. Paul received his B.S. in economics from the University of Michigan.
- Working Papers
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- Yue, Daniel, Paul Hamilton, and Iavor Bojinov. "Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development." Harvard Business School Working Paper, No. 23-029, December 2022. (Revised April 2023.) View Details
- Cases and Teaching Materials
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- Parzen, Michael, Alexander Farrow, Paul Hamilton, and Jessie Li. "Sparking Innovation in the U.S. Air Force." Harvard Business School Case 624-002, August 2023. (Revised August 2023.) View Details
- Bojinov, Iavor, Michael Parzen, and Paul Hamilton. "On Ramp to Crypto." Harvard Business School Case 623-040, October 2022. (Revised June 2023.) View Details
- Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Prediction & Machine Learning." Harvard Business School Technical Note 622-101, March 2022. (Revised July 2022.) View Details
- Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Causal Inference." Harvard Business School Technical Note 622-111, June 2022. (Revised July 2022.) View Details
- Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Linear Regression." Harvard Business School Technical Note 622-100, March 2022. (Revised July 2022.) View Details
- Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Statistical Inference." Harvard Business School Technical Note 622-099, March 2022. (Revised July 2022.) View Details
- Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Exploratory Data Analysis." Harvard Business School Technical Note 622-098, March 2022. (Revised July 2022.) View Details
- Parzen, Michael, and Paul Hamilton. "Introduction to Linear Regression." Harvard Business School Technical Note 621-086, June 2021. View Details
- Parzen, Michael, and Paul Hamilton. "Probability Distributions." Harvard Business School Technical Note 621-704, February 2021. View Details
- Bojinov, Iavor I., Chiara Farronato, Janice H. Hammond, Michael Parzen, and Paul Hamilton. "Precision Paint Co." Harvard Business School Case 622-055, August 2021. View Details
- Datar, Srikant M., Amram Migdal, and Paul Hamilton. "IBM: Design Thinking." Harvard Business School Case 121-007, April 2021. (Revised June 2021.) View Details
- Parzen, Michael, and Paul Hamilton. "The FIRE Savings Calculator." Harvard Business School Case 621-087, January 2021. View Details
- Datar, Srikant M., Sarah Mehta, and Paul Hamilton. "Applying Data Science and Analytics at P&G." Harvard Business School Case 121-006, July 2020. View Details
- Grushka-Cockayne, Yael, Michael Parzen, Paul Hamilton, and Steven Randazzo. "Kaggle 2019 Data Science Survey." Harvard Business School Case 620-091, January 2020. View Details
- Research Summary
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- Teaching
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- Served as a teaching fellow; assisted MBA students with classroom coding exercises.
- Developed course materials, including new case studies, technical notes, and code notebooks students used to analzye case data.
- Developed interactive web applications to illustrate important concepts from statistics and machine learning.
Course Overview:
Over the past decade, numerous firms have extensively invested in developing business infrastructure to collect, store, and analyze data effectively. A wide range of roles across finance, marketing, human resources, operations, innovation, and strategy now rely heavily on data for critical decision-making input and implementation. Indeed, many firms strategically differentiate themselves by their ability to translate their vast amounts of data into meaningful insights that help them gain an edge over their competitors.The Data Science for Managers (DSM) I course provides students with the necessary foundations to effectively derive and evaluate data-driven insights to inform managerial decisions. In this hands-on course, students will learn how to view and solve business problems from a data perspective. The course will introduce fundamental principles that will enable MBA graduates to understand the opportunities and limitations of analytics, develop a familiarity with programming (using the R language), and build a robust data analytics mindset.
Importantly, students will have opportunities to see how data science is used across a broad range of business environments. The course will focus on managers’ roles in data science projects, including hypothesis generation and testing, model design, interpretation of results, and the formulation of actionable recommendations.
Data science is an interdisciplinary field that combines principles from statistics and computer science with substantive domain knowledge to extract useful insights from data. The tools, technologies, and methodologies employed in data science are numerous; however, they broadly fall into four categories that mimic the typical process flow of a data science project and comprise the course’s four modules.
Course Overview:
Business Analytics has become a core function in many firms today and is driving innovation in the form of new business and operating models. Data-driven decision-making requires understanding of statistics, computer science, data visualization and data curation, communications, and ethics. A business analyst who understands all of these components is key in enabling productive conversations among engineers, statisticians and the various business functions in an organization.Understanding statistics and modern computing methods is a great asset, but creating the value from this asset requires knowing how to ask and answer the right questions. Choosing the right question and solving a problem appropriately require a deep understanding of the business context as well as familiarity with the subtleties of working with data and applying statistical methods. First, one must understand the business context from a business model or operating model perspective. Second, one must figure out how to use data and analytics to help inform a solution to the identified problem. Finally, managers and leaders should develop the capability to communicate insights from the analysis to the various stakeholders.
Applied Business Analytics targets students who want to build an understanding of how data and analytics are being used to drive decisions in a variety of industrial and organizational contexts. The course is intended for both novices and students with previous exposure to data science and analytics. During 28 sessions, students will learn foundational Business Analytics concepts, tools and techniques, and will have hands-on experience using Tableau, R, and SQL. The course will focus on business applications, including a manager’s role in hypothesis formation, model design, the interpretation of results, and the formulation of actionable recommendations.
Paul is primarily interested in teaching data science to management students through the case method. This includes technical topics (programming and statistics) as well as higher-level management issues (digital transformation, data governance, etc.) As a research associate at HBS he helped develop the following courses, which were taught in the MBA program: Applied Business Analytics, Introduction to Data Science, and Data Science for Managers. He also worked on course materials for the Operations and Supply Chain Management course in the Harvard Business Analytics Program (HBAP), and served as a teaching fellow for Stat 104 at the extension school. - Additional Information
- Area of Study
- Areas of Interest
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- analytics
- artificial intelligence
- big data
- machine learning
- technology management
- IT strategy
- analytics
- artificial intelligence
- big data
- distributed innovation
- information technology
- infrastructure
- innovation
- knowledge management
- machine learning
- open source
- organizational change and transformation
- team analytics
- technological change
- technological innovation
- technology management
- technology strategy
Additional Topics