Research Associate/Research Scientist in Biostatistics
Harvard T.H. Chan School of Public Health
The Department of Biostatistics at the Harvard T.H Chan School of Public Health invites applications for a Lead Data Scientist position for Francesca Dominici’s Lab. The work will involve close collaboration with Dr. Francesca Dominici, co-Director of the Harvard Data Science Initiative and Clarence James Gamble Professor of Biostatistics, Population and Data Science (https://www.hsph.harvard.edu/francesca-dominici/), and her team. The team’s current research focus is on the development and implementation of statistical and machine learning methods to estimate causal effects of environmental exposures on health in the presence of high dimensional data, multiple exposures, strong confounding, and complex, non-linear relationships between exposures, confounders, and health outcomes. The team has access to many rich datasets in these areas, including high resolution air pollution data and health data from Medicare, Medicaid, and private insurance companies, to which these methods will be applied in order to answer high impact scientific questions. The team is committed to conducting reproducible research and providing software and resources to implement new methods to the greater research community. Thus, a special focus of this position will be on overviewing the design and implementation of scalable software tools and reproducible workflows.
The ideal candidate is an experienced data scientist who can manage a team of junior data scientists and data analysts, and overview the efforts of Dominici’s Lab in expanding an unprecedented Open Science Research Data Platform.
This is a leadership position that comes with a competitive salary (depending on skills and experience).
Duties and responsibilities.
The Research Associate or Research Scientist will:
- be responsible for the maintenance and further extension of the Research Data Platform: processing large-scale data inputs from a variety of public and private sources and at different spatial and temporal resolutions.
- coordinate the efforts of the team in terms of statistical software development, software dissemination, and reproducible research.
- supervise the efforts of implementing statistical and machine learning methods for the analyses of environmental health data.
- lead the development and maintenance of R packages and datasets, and innovative web-based data visualizations.
- supervise and provide high-quality implementations of quantitative models to real data and will also write, and contribute to writing, scientific articles and research proposals.
- instruct and mentor PhD students and postdoctoral fellows in the above-mentioned areas and will also be involved and make decisions regarding hiring postdoctoral fellows and developers for the Dominici’s Data Science team.
- write, and contribute to writing, scientific articles and research proposals.
- PhD degree in Statistics, Biostatistics, Computer Science, Data Science.
- Strong background in applied statistics and computational methods.
- Expertise in open-source software, reproducibility and data management.
- Expertise in high-performance computing.
- Experience in managing teams of data scientists.
- Experience with GIS and spatial methods.
- Experience with high-performance computing on clusters and clouds.
- 1-3 years of work experience beyond PhD would be valued.
- Demonstrated ability to develop of new statistical methodology, inference algorithms, and machine learning techniques.
- Mastery with multiple data science tools and statistical software (R, Shiny, GIS, d3, Python, SQL, SAS, …), and ability to learn new tools as required.
- Proven experience in creating and maintaining R packages.
- Proven experience in handling massive datasets is highly desirable.
- Visa sponsorship could be considered for the suitable candidate.
Administrative questions regarding this position can be sent to Susan Luvisi at firstname.lastname@example.org.
Scientific questions regarding this position can be sent to Christine Choirat at email@example.com.
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.