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"Postdoctoral Fellow, Digital, Data, and Design Institute - Laboratory for Innovation Science"

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Postdoctoral Fellow, Digital, Data, and Design Institute - Laboratory for Innovation Science

School:

Harvard Business School

Position Description

The Digital Data Design (D^3) Institute and the LISH/Data Science & AI Operations Lab seek enthusiastic Postdoctoral Fellows skilled in computer science, statistics, operations research, or related computational fields. As part of an interdisciplinary research team dedicated to advancing management science, the fellows will develop novel quantitative methods at the interface of statistical learning, experimental design, and optimization to address challenges created by the operationalization of AI within partner organizations.

The Postdoctoral Fellows will play a pivotal role within the LISH/Data and Science Operation Lab team, collaborating with Harvard Business School (HBS) and D^3 faculty and researchers to develop methods that underpin modern organizations. A unique aspect of the lab is that it fosters collaborations among operations researchers, statisticians, and computer scientists to overcome the methodological challenges posed by the misalignment between historical methods underpinning modern data science, which were developed for significantly different contexts and applications than current AI-driven business use cases. Postdoctoral Fellows at D^3 will engage in research projects spanning Business Model Transformation, Operating Model Transformation, Organizational and Workforce Transformation, Performance and Metrics, Data Privacy and Transparency, and Societal Impact.

Candidates should bring expertise in areas such as:

  • Causal inference and the design and analysis of experiments
  • Reinforcement learning and sequential decision-making
  • Analysis of complex systems, networks, and large-scale data
  • Machine learning, generative AI, NLP, or algorithmic decision systems

Ideal applicants will have a strong background in operations research, statistics, or computer sciences and the ability to work across disciplinary boundaries to generate rigorous, high-impact insights.

There are no teaching obligations for this position.

Basic Qualifications

A Ph.D. or equivalent degree in Statistics, Operations Research or Management, Computer Science, or Information Systems. All degree requirements must be satisfied prior to the fellowship start date.

PLEASE NOTE: If you have obtained your Ph.D. in the past 12 months you must be able to provide a certificate of completion from the degree-granting institution OR a letter from the institution's Registrar stating all requirements for the degree have been successfully completed and should verify the date the degree has been conferred. No exceptions.

Special Instructions

Application Details:

Applications will be accepted until positions are filled.

Please apply here via the Harvard system. Please do not contact lab faculty; if you have any questions, please contact d3@harvard.edu.

All applications must include the following:
* Curriculum vitae
* Statement of research
* Two representative publications
* Two letters of recommendation

The review process will begin on January 7, and interviews will occur on a rolling basis. Candidates may be asked to undergo an assessment as part of the interview process.

Additional Information:

These are term positions starting July 2026 (start date flexible) through June 30, 2027, with the possibility of renewal based on funding and performance. Relocation funding not provided.

This role is offered as a hybrid (some combination of onsite and remote) where you are required to be onsite at our Boston, MA based campus. Specific days and schedule will be determined between you and your manager.

Please note that we will be conducting interviews virtually (phone and/or Zoom) for selected candidates.

Contact Information

Harvard Business School
Soldiers Field Road
Boston, MA 02163

Contact Email: kdestadler@hbs.edu

Salary Range

$75,000 annually

Minimum Number of References Required:

2

Maximum Number of References Allowed:

4
10

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