Pharmacoepidemiology Biostatistician

Nextonic Solutions is seeking a highly ambitious, adaptable, structured, and detail-oriented Biostatistician, Pharmacoepidemiology to join our vibrant team at the National Institutes of Health (NIH), National Center for Advancing Translational Science (NCATS), located in Bethesda, MD.

Responsibilities

• Provide operational, executional, tactical, and strategic support for the Good Algorithmic Practice Initiative.
• Help to implement recommendations from the Good Algorithmic Practice Initiative focused on standardization, reproducibility, and data transparency.
• Help develop standards for the operationalization and fit-for-purpose use of AI/ML methods applied to Real-World Data (RWD). Produce exemplary educational material, including tools geared toward under-served communities and new learners, covering topics relevant to use of RWD including but not limited to treatment of missing data, case control selection, minimization of type I/II error and bias, prevention of model overfitting and poor generalization and approaches to Electronic Health Record (EHR) data limitations.
• Manage, schedule, and oversee outreach initiatives to underserved communities and extramural sites with limited bioinformatics capacities.
• Support investigations from the Office of the Director and N3C leadership requiring biostatistical and/or machine learning knowledge and domain expertise. Outputs of these efforts would serve as exemplars of Good Algorithmic Practice and may result in publications.
• Produce criteria to evaluate the incremental benefit of PPRL-linked datasets (i.e., improvements in the quality and/or scope of analyses of N3C data, reduction in data missingness, etc.).
• Participate in N3C Enclave Knowledge Store object and code template peer review, integration and unit testing, and validation.

Qualifications

• PhD/MS in computer science, (bio)statistics, or similar quantitative degree.
• Proven track record of working with real world clinical data.
• Experience in modern machine learning techniques and biostatistics. Preferably experienced in causal inference methods, supervised, and unsupervised learning.
• Fluent in Python and R with knowledge of Scikit-learn and at least one deep learning framework (TensorFlow/Keras or PyTorch). Systems biology modeling experience a plus.
• Strong oral and written communication skills and ability to actively participate in technical discussions and follow up on action items.