HARVARD UNIVERSITY

Data Science Fellow

Location
Massachusetts, United States
Posted
08 Mar 2019
End of advertisement period
05 Apr 2019
Ref
8901
Contract Type
Permanent
Hours
Full Time

School

Faculty of Arts and Sciences

Department/Area

The Institute for Quantitative Social Science

Position Description

The Automated History Archive – a Harvard initiative to automate the conversion of image scans of historical quantitative publications into classified, machine-readable data models – is seeking a Data Science Fellow. Many of the biggest challenges that our society faces have their roots in the past, and history can provide fundamental insights into their causes and potential solutions. While there have been major advances in cataloging past knowledge, vast amounts of historical quantitative data that could shed light on important issues, such as inequality, social upheaval, and economic growth, remain locked in hard copy due to prohibitive curation costs. Automation and open collaboration can unlock vast disaggregated data, spurring research on a diverse array of important social science questions.

A successful automation pipeline for converting raw image scans into classified data models requires integrating computer vision tools that can recognize highly irregular data structures in the raw images with machine learning techniques for classifying digitized table fragments. Building on our initial experimentation with automating the digitization and classification of complex historical publications, we will be using tools from computer vision and machine learning to automate the conversion of historical quantitative documents into classified, machine-readable datasets on a large scale. The output will be deposited in a collaborative data platform.

The Fellow will work on algorithms for assembling digitized table fragments into classified data models. Excellent programming and problem-solving skills, as well as a solid background in machine learning, are required. The Fellow must be self-directed and able to apply the relevant research frontiers to this use case. The ideal candidate will be planning to apply to PhD programs in economics, computer science, or some other quantitative field and would benefit from spending time working in a university setting on problems at the intersection of technology and academic research.

This is a full-time position. A bachelor’s degree is required. The start date is flexible, and the duration of the position is for one year, with possibilities for extension depending on funding availability and performance. The Fellow will sit with other team members at the Institute for Quantitative Social Science, which offers a rich community of researchers working on data science problems with applications to the social sciences. The Fellow will work closely with the PI Professor Melissa Dell (Harvard Economics Department). The position provides an excellent opportunity to hone data science skills by applying them to important social questions.

Basic Qualifications

  • A bachelor’s degree is required
  • Excellent programming and problem-solving skills, as well as a solid background in machine learning, are required.
  • The Fellow must be self-directed and able to apply the relevant research frontiers to this use case.
  • The ideal candidate will be planning to apply to PhD programs in economics, computer science, or some other quantitative field and would benefit from spending time working in a university setting on problems at the intersection of technology and academic research.

Special Instructions

TO APPLY:

PLEASE DO NOT APPLY ONLINE.

Interested candidates should send a CV, unofficial transcript, and one-page cover letter describing their experience with data science to the project lead, Professor Melissa Dell: melissadell@fas.harvard.edu
Only applicants who follow these instructions will be considered

Contact Information

Professor Melissa Dell

Contact Email

melissadell@fas.harvard.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, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions or any other characteristic protected by law.