Postdoctoral Fellow in Data Science
Harvard University Faculty of Arts and Sciences
United States

Postdoctoral Fellow in Data Science at the Laboratory for Innovation Science at Harvard (LISH)

School Faculty of Arts and Sciences

Department/Area LISH/IQSS

Position Description

The Laboratory for Innovation Science at Harvard (LISH) is looking for an energetic postdoctoral fellow in data science to facilitate research analyzing crowdsourcing at scale within an enterprise organization. LISH and partners design and execute crowdsourcing competitions to study how and why crowdsourcing works while simultaneously implementing proven, real-world results.

The Data Science Postdoctoral Fellow will be a member of the team that works with leading crowdsourcing experts to analyze data collected from various activities in that field, including contests and aggregated intelligence. As such, the fellow will apply computational techniques to analyze various aspects of the crowdsourcing process, including, – but not limited to – problem sourcing and framing via data mining and literature reviews, as well as solution development at both the micro and macro level. Problems and solutions will focus on include processes, workflows, and operations in a large enterprise organization, in addition to the development and application of artificial intelligence and novel technologies in the energy industry. The fellow will also conduct analysis on how to scale and facilitate the adoption of crowd-developed solutions in a larger enterprise environment.

The Postdoctoral Fellow will work under the supervision of LISH Directors and affiliated faculty and will have the opportunity to collaborate with LISH staff, postdoctoral fellows, and doctoral students. LISH is a Harvard-wide research program led by faculty co-directors Karim Lakhani and Marco Iansiti, Harvard Business School; Eva Guinan, Harvard Medical School; and David Parkes, Harvard School of Engineering and Applied Sciences. LISH is an interdisciplinary research lab that is focused on developing a science of innovation through the application of quantitative and field experimental methods on innovation problems faced by our partners (NASA, Harvard Medical School, Broad Institute, Department of Defense Research Labs, Topcoder, Kaggle, and other firms). Current research topics for the lab include incentives for innovation, governance and management of innovation systems, and creativity and problem-solving. Over the past 10 years, LISH, in collaboration with its partners including NASA, Harvard Medical School, Scripps Research Institute, the Broad Institute, and others have completed over 700 discrete innovation contests for a range of scientific and technical tasks including computational biology, image analysis, space science, data analytics, and ideation.


• Collect and prepare the data for crowdsourcing activities;
• Design contest models and evaluation schemes;
• Create, enhance, and maintain documentation for data, modeling choices, rationale, and results;
• Document and disseminate findings through publication of research papers and white papers.

Appointment Details:

This is a one-year term appointment through Harvard University with the possibility of renewal based on performance and funding.

This position is funded by an award administered by the Institute for Quantitative Social Science (IQSS) at Harvard.

Application Detail:

The application deadline is May 30, 2020.

Select candidates will be required to take a short assessment test.

Basic Qualifications

• Hands on knowledge of C++, Java, Python, Hadoop, MapReduce; background in Machine Learning techniques
• Ph.D. in an analytical discipline (Computer Science, Statistics, Mathematics, Physics, Computational Biology, etc.)
• Practical knowledge of analytics, computation, data analysis software (e.g., R, STATA, SPSS, SAS, etc.)

Additional Qualifications

• Interest in building algorithms and analytical models for science required
• Ability to handle multiple projects, stakeholders, and demands required
• Must be a strong team player with excellent verbal and written communication skills
• Interest in learning about how to use open innovation and prize-based competitions to solve technical problems is required

Select candidates will be required to take a short assessment test.

Special Instructions


PLEASE DO NOT APPLY ONLINE. Interested candidates are asked to please email the following items to Alexandra Kesick,

• Curriculum Vitae
• Copy of academic records (unofficial records are acceptable)
• A two-page description of relevant experience with algorithms and data analysis
• Two recently published or working papers
• Contact details of two references

The application deadline is May 30, 2020.

Only applicants who follow these instructions will be considered.

Contact Information

Alexandra Kesick

Contact Email

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
Maximum Number of References Allowed

Supplemental Questions

Required fields are indicated with an asterisk (*).

Applicant Documents

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