Biostatistician

Geisinger•Danville, PA

19h•Remote

About The Position

The Biostatistics Core at Geisinger collaborates closely with researchers across the health system, providing comprehensive support throughout all stages of the research process—from proposal development to data analysis and interpretation. Core members offer expertise in refining research questions and hypotheses, designing studies and selecting variables, estimating sample size and statistical power, developing analysis plans, designing data collection methods and randomization schemes, managing data and conducting biostatistical analyses. The Core consists of 10 members with a mix of MS and PhD level positions. Members collaborate with departments across the health system including Community Medicine, Surgery, Cardiology, Otolaryngology, Pediatrics, Women’s Health, among others and support full-time research faculty on grant related efforts.

Requirements

Doctorate degree in biostatistics or related field
At least 2 years of experience in a healthcare environment
Management experience of junior colleagues
Solid understanding of clinical research
Excellent communication skills
Ability to work independently
Ability to handle multiple projects to meet deadlines in a multidisciplinary research environment
Experience managing large datasets in a healthcare environment
Experience in programming for data management and complex data analysis using SAS or R
Experience in logistic regression, time-to-event (survival), and observational data analysis methods
Fundamentals of model building

Nice To Haves

Master’s degree in biostatistics or a closely related field and 10 years of experience
PhD = 4 years of relevant experience

Responsibilities

Collaborating on the design of clinical research studies
Writing biostatistical analysis plans for research proposals and grants
Calculating sample size and power, managing, analyzing, and interpreting study data and results
Contributing to study publications
Educating members of the research team in statistical methods and study design