Research Scientist IV, Pardes Scholar

New York State Psychiatry Institute•New York, NY

1d•$114,700 - $136,655

About The Position

The Research Foundation for Mental Hygiene is seeking a qualified candidate to fill a full-time Research Scientist IV, Pardes Scholar position. The successful candidate for this position will conduct data analysis using advanced machine learning supervised and unsupervised modeling with secondary data to identify phenotypes among individuals with schizophrenia and bipolar disorder. Then among these phenotypes they will identify efficacious treatment regimens resulting in treatment rules for individuals with these unique phenotypic features that have positive responses to these medication regimens. This work will be in support of a K23 grant and a Brain and Behavior Research Foundation Young Investigator Award. Data from Medicaid and Medicare and from the New York Presbyterian Data Warehouse will be analyzed. With these data, the candidate will use machine learning methods including topic modeling, random forests, support vector machines and deep learning to identify clusters of individuals with schizophrenia and bipolar disorder and the psychotropic and psychosocial treatments they are receiving. They will then conduct comparative effectiveness studies using pharmacoepidemiology methods for the regimens taken within each cluster of individuals to identify those that are most effective.

Requirements

Bachelor's degree and five years of professional research experience in an appropriate field or master's degree in an Social Work may substitute for one year of experience, or Ph.D. in epidemiology with a sub-focus in psychiatric epidemiology may substitute for an additional year of experience.

Nice To Haves

Previous experience conducting multivariate and structural equation modeling analyses as well as survival analyses and multi-level modeling
Experience conducting secondary data analyses with Medicaid data, publicly available survey data, and clinical trials from the NIMH Data Archive.
Training in graduate level machine learning methods.
Experience using the R statistical program.
Expertise in the use of SAS or Stata.

Responsibilities

Data analysis using advanced machine learning supervised and unsupervised modeling with secondary data to identify phenotypes among individuals with schizophrenia and bipolar disorder.
Identify efficacious treatment regimens resulting in treatment rules for individuals with these unique phenotypic features that have positive responses to these medication regimens.
Analyze data from Medicaid and Medicare and from the New York Presbyterian Data Warehouse.
Use machine learning methods including topic modeling, random forests, support vector machines and deep learning to identify clusters of individuals with schizophrenia and bipolar disorder and the psychotropic and psychosocial treatments they are receiving.
Conduct comparative effectiveness studies using pharmacoepidemiology methods for the regimens taken within each cluster of individuals to identify those that are most effective.