Biostatistician

Description

The Center for Population Neuroscience and Genetics at the Laureate Institute for Brain Research is seeking an experienced, talented and highly motivated biostatistician to develop statistical analyses of high-dimensional datasets, including longitudinal observational data and electronic health records. Analyses will be implemented primarily in R and Python.

The profile of research organization

Laureate Institute for Brain Research (LIBR) has been a pioneer on the mind-body interaction and how it relates to psychiatric symptoms. The LIBR-based Center for Population Neuroscience and Genetics (PNG) is positioned to use large-scale population data, including biobanks, national registries, and deep-phenotyped large-scale longitudinal cohorts, to investigate the etiology of neurodevelopmental disorders and finding the potential pathways for clinical interventions.

Position

We are currently seeking a biostatistician who possesses experience in large-scale data wrangling and statistical analyses. The Statistician, under the direction of Dr. Thompson, will program statistical analyses using the R programming language (and to a lesser degree Python). This will include formatting and processing multi-modal, longitudinal data for integrated statistical analyses and producing data QC reports as well as complex analyses. Analyses implemented will include mixed-effects models, latent variable models, dimension reduction algorithms, prediction models, and survival model, among others. The Statistician will also support analyses of several large-scale Electronic Health Record (EHR) databases.

This position provides unique experience with longitudinal data aimed at understanding of neuropsychiatric clinical course, as well as how the brain develops and is affected by exposure to substances and other environmental, social, and biological factors during pregnancy and after birth. What we learn from this research will have lasting impacts on future generations of children and adults.

Qualifications

Necessary qualifications

· A relevant degree or equivalent qualifications.

· Experienced in the R Statistical Language

· Background in applied statistics and high-dimensional data analytics

Preferred Qualifications

· Excellence in large scale, population level data analysis

· Experience in Python

· Experience in developing methods for high-dimensional data

· Experience in analyzing multivariate longitudinal and survival data

· Experience in Neuroimaging and Genomics

· Experience in developing methods for high-dimensional data analysis

Personal Qualifications

· Strong drive to obtain established goals

· Excellent collaborative, communication, and presentation skills

· Scientifically self-motivated and creative

· Ability to work both independently and in structured groups

· Open-minded and analytical personality