Senior Pharmacoepidemiologist

(ID: 2025-0846)

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 talented Pharmacoepidemiologist to join our team to support projects at the NIHs 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.

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)

Overview:

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.
• 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.

Required Qualifications:

• 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.
• 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.

Preferred Qualifications:

• 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.

Disclaimer: The above description is meant to illustrate the general nature of work and level of effort being performed by individuals 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 Axles 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:

This role has a market-competitive salary with an anticipated base compensation range listed below. Actual salaries will vary depending on a candidates experience qualifications skills and location.

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