Axle is a bioscience and information technology company that offers advancements in translational research, biomedical informatics, and data science applications to research centers and healthcare organizations nationally and abroad. With experts in biomedical science, software engineering, and program management, we focus on developing and applying research tools and techniques to empower decision-making and accelerate research discoveries. We work with some of the top research organizations and facilities in the country including multiple institutes at the National Institutes of Health (NIH).
Axle is seeking a Biostatistician, Pharmacoepidemiology to join our vibrant team at the National Institutes ofHealth (NIH) supporting the National Center for Advancing Translational Sciences (NCATS) located in Rockville, MD.
Benefits We Offer:
We are looking for a skilled and motivated biostatistician/pharmaco-epidemiologist to lead Real World Evidence (RWE) course development for the National Covid Cohort Collaborative (N3C) under a new program: AIM-AHEAD. The N3C created the AIM-AHEAD program to convert the scientific, technical and methodological learnings into curriculum to help future investigators, researchers and collaborators. The N3C contains hundreds of treatment effectiveness studies using electronic health records and/or claims data to support projects at the NIH. Curriculum design activity from these projects will require expert-level pharmacoepidemiologists to evaluate, curate and prepare content. Publication opportunities may arise from the curriculum development focus.
The position will be based at Axle in Bethesda, Maryland, but functionally remote from US locations, with occasional travel. The successful applicant will be involved in designing and producing RWE curriculum using N3C observational studies in support of the National Center for the Advancement of Translational Science (NCATS).
The ideal candidate will have experience in the application of causal inference methods (IPTW, PSM, G-methods, etc.) and observational study designs (target trial emulation, self-controlled case series, test-negative design, etc.) They will have experience leading RWE projects from start to finish and as author for publications in the field. The candidate will have a deep understanding of the limitations of Real World Data (RWD) and various forms of bias and confounding that arise in observational studies (immortal time bias, confounding by indication, time-varying confounding, etc…). They will work with clinicians, biostatisticians, and other stakeholders to identify educational curriculum of significant public health importance. They will stay current with scientific and technical literature and remain informed about new approaches and methodologies, including participation in conferences or workshops, independently evaluating scientific resources, and potentially publishing scientific papers from this work.
Responsibilities
Develop RWE curriculum for AIM-AHEAD extension of N3C.
Collaborate with instructional designers to create new epidemiology courseware.
Serve as a RWE subject matter expert to Axle and NCATS leadership.
Implement recommendations from leadership on standardization, reproducibility, and data transparency.
Develop standards for the operationalization and fit-for-purpose use of Causal and AI/ML methods applied to Real-World Data (RWD).
Produce criteria to evaluate the incremental benefit of PPRL-linked datasets (i.e., improvements in the quality and/or scope of analyses, reduction in data missingness, etc.).
Participate in code and documentation peer review, integration, unit testing, and validation.
Produce exemplary material relevant to use of RWD including but not limited to treatment of missing data, case control selection, minimization of type I/II error, bias, prevention of model overfitting and poor generalization and approaches to Electronic Health Record data limitations.
Participate in preparing reports, visualizations, proposals and publications.
Present your work internally at Axle, to the NIH and at selected conferences.
Support investigations requiring biostatistical and/or machine learning knowledge and domain expertise. Outputs of these may result in publications.
Lead the design and execution of RWE studies using observational data and/or materials produced from AIM-AHEAD curriculum design.
Qualifications
Ph.D or Postdoc preferred.
Minimum MS in computer science, (bio)statistics, or similar quantitative degree, or MPH with experience in biostatistical programming and pharmaco-epidemiologic study design and fulfillment.
Proven track record of working with real world clinical data.
Familiarity with medical coding systems (ICD, CPT, HCPCS, NDC, LOINC, SNOMED, etc.).
Experience in modern machine learning techniques and biostatistics. Preferably experienced in causal inference methods, supervised, and unsupervised learning.
Fluent in R and Python with knowledge of SQL and data management. Systems biology modeling experience is a plus.
Strong oral and written communication skills and ability to actively participate in technical discussions and follow up on action items.
Sound judgment, positive attitude, and creative outlook required.
NIH experience preferred.
Disclaimer:The above description is meant to illustrate the general nature of work and level of effort being performed by individual’s assigned to this position or job description. This is not restricted as a complete list of all skills, responsibilities, duties, and/or assignments required. Individuals may be required to perform duties outside of their position, job description or responsibilities as needed.
The diversity of Axle’s employees is a tremendous asset. We are firmly committed to providing equal opportunity in all aspects of employment and will not tolerate any illegal discrimination or harassment based on age, race, gender, religion, national origin, disability, marital status, covered veteran status, sexual orientation, status with respect to public assistance, and other characteristics protected under state, federal, or local law and to deter those who aid, abet, or induce discrimination or coerce others to discriminate.
Accessibility: If you need an accommodation as part of the employment process please contact: careers@axleinfo.com