Post-Doctoral Fellow in Data Science

Location
Cambridge, MA, USA
Salary
Competitive
Posted
Dec 06, 2017
Closes
Jan 06, 2018
Ref
1027533945
Contract Type
Full Time
Details

Title
Post-Doctoral Fellow in Data Science

School
Faculty of Arts and Sciences

Department/Area
Institute for Quantitative Social Science (IQSS)

Position Description

The Laboratory for Innovation Science at Harvard (LISH) is looking for an energetic data scientist to facilitate research analyzing crowd data and science production function. LISH and partners implement crowdsourcing competitions to study how and why crowdsourcing works while simultaneously deploying real-world results.

LISH, since its founding in 2010 as the NASA Tournament Lab, has led the way in the application of crowdsourcing approaches to solve important technological problems. Over the past 7 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.

The Data Science Postdoctoral Fellow will be a member of the team that works with leading crowdsourcing experts to analyze data collected from various crowdsourced activities, including contests and aggregated intelligence. The postdoc will also apply computational techniques to study the process of scientific publishing, particularly peer review. The postdoc will apply data-mining and natural language processing (e.g., topic modeling) techniques to a large corpus of biomedical literature in order to develop meaningful measurements/features of content and novelty of scientific works. The postdoc will use the corpus and external data to validate these features and use them in prediction and classification tasks. Necessary techniques and tools may include Python, R, Hadoop/mapreduce, etc.

Responsibilities:

• Prepare the data and solution-testing software
• Create, enhance, and maintain documentation for data, modeling choices, rationale, and results
• Document findings through research papers

Key Members:
This position will work under the supervision of Professor Karim Lakhani, Harvard Business School and alongside other postdoctoral fellows and the lab's Data Science team. Jin Paik is the program director at the lab.

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

Basic Qualifications

• Hands on knowledge of C++; familiarity with Java and Python is a plus

• Ph.D. in analytical discipline (Computer Science, Statistics, Mathematics, Physics, Computational Biology, etc.) 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 institute's registrar stating all requirements for the degree have been successfully completed and should verify the date the degree has been conferred. No exceptions.

• Interest in building algorithms and analytical models for science

• Practical knowledge of analytics, computation, data analysis software (e.g., R, STATA, SPSS, SAS, etc.)

• Ability to handle multiple projects, stakeholders, and demands

• Strong team player with excellent verbal and written communication skills

• Interest in learning about how the use of open innovation and prize-based competitions to solve technical problems

Additional Qualifications

Special Instructions

Contact Information

The application deadline is 12/19/17. Please email the following items to Jin Paik, jpaik@hbs.edu:

• Curriculum Vitae
• Copy of study records (unofficial records are acceptable)
• A one-page description of relevant experience with algorithms and data analysis.
• Contact details of three references

Contact Email
jpaik@hbs.edu

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, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law.

Minimum Number of References Required

Maximum Number of References Allowed

Supplemental Questions