Benefits We Offer:
- 100% Medical, Dental & Vision Coverage for Employees
- Paid Time Off and Paid Holidays
- 401K match up to 5%
- Educational Benefits for Career Growth
- Employee Referral Bonus
- Flexible Spending Accounts:
- Healthcare (FSA)
- Parking Reimbursement Account (PRK)
- Dependent Care Assistant Program (DCAP)
- Transportation Reimbursement Account (TRN)
We are seeking a talented Pharmacoepidemiologist to join our team to support projects at the NIH’s National Center for Advancing Translational Sciences (NCATS). In this role, you will collaborate with clinical and data scientists, methodologists, and software engineers to develop and execute exemplar causal inference studies. You will work as a subject matter expert to create a real-world evidence (RWE) methods decision tree; support master protocol development; and support development of gold-standard guides and methodologies for conducting best practices causal inference research. You will: design, execute, and analyze studies using the National Clinical Cohort Collaborative (N3C) data; review and recommend strategies for selecting study designs to answer causal inference questions; help establish, and implement appropriate analytic value sets.
The ideal candidate for the Pharmacoepidemiologist position is a highly skilled professional with a Ph.D. in Pharmacoepidemiology, Epidemiology, Biostatistics, Causal Inference or related field. This person has experience with electronic health record and/or claims data and a strong understanding of observational study principles including: missing data handling methods; temporal research questions (cross-sectional, longitudinal); causal contrast of interest (e.g., intent-to-treat, per-protocol); effect measure of interest (e.g., risk ratio, hazard ratio); and estimands (e.g., average treatment effect, average treatment effect in the treated) of interest. Your research experience includes one or more of the following: target trial emulation, sequential trial analysis, marginal structural models, longitudinal matching, G methods, or equivalent causal inference methods, realized in multiple primary-author publications. Fluency with coding languages and tools is expected (i.e. SQL, Python, and R).
Responsibilities:
- Serve as SME for RWE causal inference studies.
- Design and execute studies with N3C EHR data considering the following: extent of missing data; missing data handling methods; temporal component of research studies (i.e. cross-sectional, longitudinal); the causal contrast of interest (e.g., intent-to-treat, per-protocol); the effect measure of interest (e.g., risk ratio, hazard ratio); and estimand (e.g., average treatment effect, average treatment effect in the treated) of interest.
- Develop and operationalize decision-support frameworks to standardize how causal inference studies are designed, executed, and evaluated across projects.
- Contribute to the development of reusable, platform-level components that support reproducible and scalable RWE generation.
- Review and recommend strategies for selecting individuals in clinical cohorts.
- Ensure the highest data quality and minimization of bias.
- Use tools and languages such as Python, R, SQL, and Spark.
- Help establish and implement appropriate analytic value sets.
- Collaboratively produce extensive documentation (i.e. through training material, research protocols, open data sources such as repositories and/or methodological and research papers).
- Work closely with data science and software developer teams to articulate technical methods and implement decision tool strategies.
- Extract and transform clinical data from clinical trial and observational studies.
- Participate in project meetings, provide updates on progress, challenges, and mitigation strategies.
- Actively engage in key relevant meetings and keep abreast of emerging best practices and methods.
Qualifications
Must haves:
- Ph.D. in Pharmacoepidemiology, Epidemiology, Biostatistics or related field.
- Experience with the following: target trial emulation, sequential trial analysis, marginal structural models, longitudinal matching, G methods, or equivalent causal inference methods realized in at least 2 primary-author peer-reviewed publications, or equivalent.
- Experience developing or applying structured methodological frameworks to guide observational research.
- Ability to independently conduct statistical analysis with analytic software tools (e.g. Python, R, SQL, MatchIt/WeightIt, Matplotlib, Lifelines, SKLearn, GT-summary, statsmodels, Survey).
- Fluent in coding languages and tools (e.g. Python, R, SQL, Spark).
- Strong communication and collaboration skills, with the ability to work effectively in a multidisciplinary team.
Nice to haves:
- Experience with meta-analyses and/or systematic reviews.
- Expertise in best practices for statistical approaches, data wrangling and visualization.
- Knowledge of FAIR data principles.
- Clinical informatics experience.