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Postdoctoral Data Scientist: GIS and mHealth

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Title Postdoctoral Data Scientist: GIS and mHealth
School Harvard T.H. Chan School of Public Health
Department/Area Biostatistics
Position Description

The Department of Biostatistics at the Harvard T.H Chan School of Public Health invites applications for a Postdoctoral Data Scientist position to work on large scale environmental health and mobile health geospatial data. The position will involve close scientific collaboration with Drs. Peter James, Antonella Zanobetti, and Corwin Zigler. There will also be ample opportunities to interact with the Harvard Center for Geographic Analysis (
The successful candidate will be responsible for coordinating geospatial analyses for very large scale heterogeneous data such as: geocoded nationwide prospective cohort studies, atmospheric chemistry model outputs, and temporally-dense measurements from smartphone applications and wearable devices. The successful candidate will also interact with about 15 PhD students and postdoctoral fellows.
The ideal candidate is an independent, solution-oriented thinker with a strong background processing very large data sets, applying analytical rigor and statistical methods, and driving toward actionable insights and novel solutions.
Duties and Responsibilities:
-The Data Scientist will contribute to the effort of:
Benchmarking and identifying the best stack to handle and analyze massive amount of geospatial data, some containing protected health information.
Processing massive amounts of geospatial data such as millions of atmospheric transport trajectories or mobile health measurements (GPS, physical activity, heart rate, sleep, etc. from smartphones and wearable devices) for tens of thousands of study participants.
Developing and disseminating software for reproducible research.
Creating innovative web-based data visualizations.
Training other team members in advanced GIS tools and techniques.
Writing scientific articles and research proposals.

Basic Qualifications

-PhD in Statistics, Biostatistics, Computer Science, Data Science, Environmental Health, Information Technology, Environmental (or other) Engineering, Geography and Geoinformation Science, or other quantitative field.
-Strong background in applied statistics and computational methods.
-Interest in open-source software, reproducibility and data management.
-Experience in handling very large spatial datasets.
-Familiarity with multiple data science tools (R, Shiny, GIS, d3, Python, PostgreSQL, MongoDB,…), and ability to learn new tools as required.

Additional Qualifications

-Demonstrated ability to contribute to research of new statistical approaches, inference algorithms, and machine learning techniques.
-Experience with version control systems, in particular Git and GitHub.
-Knowledge of SAS would be helpful, mostly to be able to reuse and modify SAS code that the team is already using.
-Interest in mobile health technology, including smartphone applications and wearable devices to develop temporally-dense measures of location and behavior.

Special Instructions

The position is funded for one year with strong possibility of renewal.
Please submit online:
-a cover letter
-a curriculum vitae
-the name and contact information of three references
-and possibly links to code portfolios such as GitHub
Administrative questions regarding this position can be sent to Susan Luvisi at
Scientific questions regarding this position can be sent to Christine Choirat at

Contact Information

Susan Luvisi

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, 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

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