The Department of Biostatistics at the Harvard T.H. Chan School of Public Health invites applications for a Postdoctoral Fellow position for data science and/or statistical methods development to address questions of climate change and air pollution regulatory policy. Expertise in causal inference and machine learning is desirable but not necessary. The Postdoctoral Fellow will work with a multi-disciplinary team with expertise in statistical methods, epidemiology, climate science, and biomedical science as part of the NSAPH group, https://www.hsph.harvard.edu/nsaph/ . The position will be under the supervision of Dr. Francesca Dominici, Professor of Biostatistics and co-Director of the Harvard Data Science Initiative.

Duties and Responsibilities
The Post-doctoral Fellow will contribute to the effort of:

· Analyzing environmental health effects in big data

· Collaborate with our biostatistics and data science group

· Writing scientific articles and research proposals

Doctoral degree in Computer Science, Statistics, Biostatistics, or related field.

Experience in analyzing real data, strong programming skills, and familiarity with statistical methods is preferred.

Excellent communication and writing skills desired.

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.

· PhD in Biostatistics, Statistics or Computer Science.

· Experience in handling very large spatial datasets.

· Experience in applied statistics and computational methods.

· Knowledge of R, and Python.

· Interest in open-source software, reproducibility and data management.

· Familiarity with multiple data science tools and ability to learn new tools as required.

· Experience with version control systems, in particular Git and GitHub.

Trevor Bierig

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.