Sr. Biostatistician

About The Department

BSD PHS - Biostatistics

Public Health Sciences (PHS) is the home in the Biological Sciences Division to biostatistics, epidemiology and health services research. These core fields in public health research share a focus on the development and implementation of complex analytic methods to understand the determinants of health, the efficacy of experimental treatments, and the structure of health care at the population level. Bringing together these fields in one department underscores their commonality and enhances opportunities for interdisciplinary research.

Faculty members lead local, national, and international studies, and also welcome opportunities to collaborate with faculty across the Biological Sciences Division and the university. Substantively, our research themes include social and environmental determinants of health, genetics and disease, the economics of health care, and the evaluation and implementation of new technologies in public health and clinical care. In terms of methodological expertise, areas in which our faculty has developed innovative approaches include: risk factor measurement;

multilevel, clustered and longitudinal data; clinical trials; administrative health data; social networks; and statistical methods to assess the genetic and molecular basis of disease.

The job performs a range of activities to support the statistical design and implementation of research analysis. Uses knowledge of statistics to communicate analyses for research proposals and draft reports detailing their results. Follows objectives and procedures for sampling, randomization, data collection, analysis, and quality control.

The Sr. Biostatistician will be responsible for assisting investigators in the design of both basic science and clinical studies, for assisting in the implementation of these studies (e.g., randomizing treatment assignment, interim monitoring of data quality and outcomes, etc.), and for analyzing the resulting data for presentation and publication. They will be responsible for creating workflows for data management and analysis that are both efficient and reproducible, and for producing publishable displays, both tabular and graphical, of data and analytic results.

The Sr. Biostatistician will also be responsible for documenting their work in the form of written reports and sections to be included in grant proposals and manuscripts for publication.

This at-will position is wholly or partially funded by contractual grant funding which is renewed under provisions set by the grantor of the contract. Employment will be contingent upon the continued receipt of these grant funds and satisfactory job performance.

• Provides statistical guidance in designing studies to answer scientific questions, including basic science experiments, animal studies, clinical trials and population-based studies.
• Advises clinical investigators in collecting, managing and analyzing their data. Generate interim analysis and statistical reports as needed.
• Assist investigators in the interpretation of the results.
• Provides support for generation of abstracts and manuscripts for publication, including writing of Statistical Methods sections.
• Performs a range of activities to facilitate the design of research project and the publication of their results. Uses knowledge of statistics to help develop objectives and statistical procedures for the project.
• Analyzes data and prepares data tables, charts, and graphs for reports. Writes grant proposals for faculty throughout the unit.
• Writes statistical computer programs and reviews computer output for consistency and quality.
• Performs other related work as needed.

Minimum Qualifications

Minimum requirements include a college or university degree in related field.

Minimum requirements include knowledge and skills developed through 2-5 years of work experience in a related job discipline.

• Master’s degree in Statistics, Biostatistics, Data Science, or Data Analytics.
• At least one course each in mathematical statistics (e.g., point estimation, confidence intervals, hypothesis testing, large sample theory, asymptotic efficiency, exponential families, decision theory, etc.), linear models, and longitudinal (or panel) data analysis. Course(s) in at least two of the following areas: generalized linear models, categorical data analysis, survey sampling, survival analysis, epidemiological methods, mixed,…