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Postdoctoral Fellow - Customer Intelligence Lab - Digital, Data, Design Institute, Harvard University
Postdoctoral Fellow - Customer Intelligence Lab - Digital, Data, Design Institute, Harvard University-March 2024
Cambridge
Mar 31, 2026
About Postdoctoral Fellow - Customer Intelligence Lab - Digital, Data, Design Institute, Harvard University

  Details

  Title Postdoctoral Fellow - Customer Intelligence Lab - Digital, Data, Design Institute, Harvard University

  School Harvard Business School

  Department/Area

  Position Description

  The Digital, Data, and Design (D^3) Institute at Harvard is accepting applications for multiple postdoctoral fellows for academic year 2024-2025 to work on research activities at our research labs. D^3 conducts research at the intersection of academia and practice. For more information on D^3, please visit https://d3.harvard.edu/labs.

  The Postdoctoral Fellows will work under the direct supervision of faculty Principal Investigators and the Senior Director of Labs. They will work closely with the lab manager and research associate(s) at each lab. D^3 is looking for candidates with diverse backgrounds and/or new perspectives. There are no teaching requirements for these open positions.

  The Customer Intelligence Lab, led by Professors Ayelet Israeli and Eva Ascarza, is seeking a Postdoctoral Fellow. The goal of the Customer Intelligence Lab is to leverage customer data to improve outcomes for firms, consumers, and society at large.

  The postdoctoral fellow will apply and develop methodologies to questions of interest in marketing and economics. A typical empirical project involves refining a research question, designing an experiment or survey, analyzing historical and experimental data, and writing targeted at peer reviewed management science journals.

  The ideal candidate will have:

  Familiarity with methodological foundations, for example: statistics and econometrics, causal inference, machine learning methods, experimental design, modeling of unstructured data, non-parametric Bayesian methods, scalable inference, statistical computation, and visualization.

  Interest in applying advanced data analysis to improve research, marketing practice, and policy-making;

  Programming/scripting knowledge suitable for processing raw data for analysis (e.g., R, Python, Julia);

  One or more computational environments for statistical analysis (e.g., R, Stata);

  Experience writing papers for management science journals.

  Basic Qualifications

  A Ph.D. involving empirical social sciences or programing (e.g., computer science, economics, marketing, machine learning, statistics, applied mathematics, or related fields).

  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.

  Strong team player with excellent communication skills

  Additional Qualifications

  Special Instructions

  Application Details:

  Applications will be accepted until the positions are filled

  Please apply here via the Harvard system. Please do not contact lab faculty; if you have any questions, please contact [email protected].

  All applications must include the following:

  Curriculum vitae

  Please including a Link to Github account and/or any software developed

  Copy of academic records (official records are acceptable)

  2-page research statement describing prior research experience and future research plans

  Two representative publications (preprints are acceptable)

  Two letters of recommendation.

  Candidates may be asked to undergo an assessment as part of the interview process.

  Additional Information:

  These are term positions starting July 2024 (start date flexible) through June 30, 2025, 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.

  Culture of Inclusion: The work and well-being of HBS is profoundly strengthened by the diversity of our network and our differences in background, culture, national origin, religion, sexual orientation, and life experiences. Explore HBS work culture at https://www.hbs.edu/employment/.

  Commitment to Equity, Diversity, Inclusion, and Belonging

  Harvard University views equity, diversity, inclusion, and belonging as the pathway to achieving inclusive excellence and fostering a campus culture where everyone can thrive. We strive to create a community that draws upon the widest possible pool of talent to unify excellence and diversity while fully embracing individuals from varied backgrounds, cultures, races, identities, life experiences, perspectives, beliefs, and values.

  EEO Statement

  We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions, or any other characteristic protected by law.

  Contact Information

  Rebecca Wickel, Recruiting Manager, Research Staff Services

  Harvard Business School

  Soldiers Field Road

  Boston, MA 02163

  Contact Email [email protected]

  Equal Opportunity Employer

  We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions or any other characteristic protected by law.

  Minimum Number of References Required 2

  Maximum Number of References Allowed 4

  Keywords

  Supplemental Questions

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