Major areas of focus will be: 1) accessing and analyzing ambulatory, in-patient clinical, and population level data, 2) overseeing and maintaining private and public disease registries that will integrate commonly collected clinical data with specialized population level data sets, 3) working with basic, clinical and translational scientists on the development of investigator-initiated protocols, and 4) obtaining, accessing and analyzing big data sets from outside sources.

The candidate will also be expected to develop and pursue a portfolio of externally funded research complementary to high priority areas of PSP including cancer, children’s health, sexually transmitted diseases and other projects of interest. The position requires expertise in behavioral and population-based research studies with the goal of changing health outcomes in central and southern Illinois through big data analysis. Big data being defined as datasets so large and complex that they don’t conform to traditional methods of analysis and storage.

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1. Ph.D. with at least 2 years post-doctoral training appropriate to their area of specialization or MD/DO combined with a master’s in biostatistics, epidemiology, psychology, bioinformatics, computer science or health services research.
2. Sensitive to the needs of underrepresented minority populations.

• Demonstrable experience in state of the art approaches to data acquisition and analysis of longitudinal clinical datasets as well as large public use epidemiological datasets such as NHANES. 
• Experience working on NIH funded grants such as R21, R03 or R01.
• Record of methodological and statistical contributions to peer-reviewed articles.
• Demonstrated competence with SAS and at least one other statistical software (e.g., STATA, R, SPSS).
• Proficient in multiple methods of data imputation, cumulative and Cox hazards modeling, survival analysis structural equation modeling, linear mixed models, generalized estimating equations, the development and validation of prediction models, and non-parametric methodologies.
• Proficient in machine learning, Bayesian statistics, relational database (such as SQL), predictive modeling and at least one computer language (Python, PHP)
• Experience with state of the art methods for analyzing big data is highly desirable.
• Above average to excellent verbal and written communication skills.
• A proven track record of working effectively on teams inclusive of diverse individuals with varying research experience and backgrounds.

Please Read the Position Description for this specific vacancy to ensure that you are interested.  To apply:

1. Select the 'Apply to Job' button at the bottom of this posting.
2. Complete the application forms attached to the vacancy.

Upon completion of ALL bulleted items, you will have officially applied for this position and your application materials will be reviewed.

• Upload: CV
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