Real World Biostatistician - RWE CMH experience

Job Responsibilities Design and execute real‑world evidence (RWE) studies using EMR and claims data; conduct data specifications, SAP, and protocol development aligned with key research objectives. Develop and apply robust statistical methodologies, including causal inference (propensity scores, weighting, matching; GLM, GLMM, MMRM), survival analysis, and machine‑learning approaches such as random forest. Implement trial emulation frameworks and establish external control arms with borrowing strategies. Perform data analysis using healthcare coding systems (ICD, NDC, etc.). Conduct sample size estimation and power calculations for observational and hybrid study designs. Collaborate cross‑functionally with HEOR, Market Access, Regulatory, and Clinical Development stakeholders to translate complex analytical results into clear, actionable insights in presentations or study reports. Support methodological innovation in RWE, including integration of machine‑learning methods where appropriate. Required Qualifications M.S. or Ph.D. in Biostatistics, Statistics, Epidemiology, or a related field. At least 5 years of experience in RWD/RWE analytics (industry or equivalent). Strong experience with EMR and/or claims data. Proficiency in healthcare coding systems (ICD, NDC, SNOMED, RxNorm). Programming expertise in at least one of SAS, R, or Python. Working knowledge of SQL logic and OMOP data structures. Solid understanding of causal inference methods and observational study design. Experience with sample size and power considerations for observational studies. Demonstrated ability to write cohort definitions in SQL, debug data issues (e.g., time‑zero alignment, exposure gaps), and translate statistical estimand logic. RWE CMH experience. Preferred Qualifications Ph.D. (preferred). Experience in one or more therapeutic areas: diabetes, cardiovascular disease, or metabolic disorders. Familiarity with trial emulation methodologies, external control borrowing/hybrid designs, and basic machine‑learning methods applied to RWD. Demonstrated ability to work across multiple therapeutic areas in a fast‑paced environment. Strong communication and stakeholder engagement skills. Advanced skills: building reusable cohort pipelines, optimizing queries for large‑scale databases, and working across multiple common data models (OMOP, Sentinel, PCORnet). Core Competencies Analytical rigor and methodological depth. Cross‑functional collaboration. Agility across diverse projects and therapeutic areas. Clear and effective scientific communication. Tasks, duties, and responsibilities as listed in this job description are not exhaustive. The Company, at its sole discretion and without prior notice, may assign other tasks, duties, and job responsibilities. Equivalent experience, skills, and/or education will also be considered so that the qualifications of incumbents may differ from those listed in the Job Description. The Company, at its sole discretion, will determine what constitutes as equivalent to the qualifications described above. Nothing contained herein should be construed to create an employment contract or to alter the relationship of the parties at any time. The Company is committed to compliance with all applicable laws, including the United States Equality Act, Title VII, the Americans with Disabilities Act, and the EU Equality Directive, among others. The Company is committed to providing reasonable accommodations to employees or applicants with disabilities, as appropriate, to assist them in performing the essential functions of the job. #J-18808-Ljbffr Syneos Health/ inVentiv Health Commercial LLC