Director Real World Evidence Rwe Jobs in Texas (NOW HIRING)

Biostatistician - Real-World Evidence (RWE)

Role Overview

We are seeking a biostatistician with strong experience in real-world data (RWD) with observational study design and safety study experience in addition to RWE CMH experience. This role will support evidence generation across multiple therapeutic areas. This role will focus on the design, analysis, and interpretation of observational studies using EMR and claims data toinform:clinical development, HEOR, regulatory strategy, and market access.

Key Responsibilities

• Design and execute real-world evidence (RWE) studies using EMR and claimsdata;Conducting data specs,SAPand protocol with key research objectives

• Develop and apply robust statistical methodologies, including:

• Causal inference methods (e.g., propensity score methods, weighting, matching; GLM or GLMM, MMRM; survival analysis; random forest)

• Trial emulation frameworks

• External control arm development and borrowing strategies

• Perform data analysis using healthcare coding systems (e.g., ICD, NDC)

• Conduct sample size estimation and power calculations for observational and hybrid study designs

• Collaborate cross-functionally with stakeholders across:

• HEOR

• Market Access

• Regulatory

• Clinical Development

• Translate complex analytical results into clear, actionable insights,e.g.powerpointor study report for decision-making

• Support methodological innovation in RWE, including integration of machine learning approaches where appropriate

Required Qualifications

• M.S. or Ph.D. in Biostatistics, Statistics, Epidemiology, or related field

• 5 years of experience in RWD/RWE analytics (industry or equivalent)

• Strong experience with EMR and/or claims data

• Proficiencyin healthcare coding systems (e.g., ICD, NDC)

• Programmingexpertisein at least one of: SAS, R, or Python

• Working knowledge with SQL logic and OMOP data structures

• Solid understanding of:

• Causal inference methods

• Observational study design

• Sample size and power considerations

• Some examples: Independently write cohort definitions in SQL logic; Debug data issues e.g., time zero alignment, exposure gaps; Understand concept mapping (ICD SNOMED RxNorm); Translate statisticalestimand censoring rule and data extraction logic

• RWE CMH Experience

Preferred Qualifications

• Ph.D. strongly preferred

• Experience in one or more therapeutic areas:

• Diabetes

• Cardiovascular disease

• Metabolic disorders

• Familiarity with:

• Trial emulation methodologies

• External control borrowing / hybrid designs

• Basic machine learning methods applied to RWD

• Demonstrated ability to work across multiple therapeutic areas (TAs) in a fast-paced environment

• Strong communicationand stakeholder engagement skills

• Advanced (nice-to-have, not alwaysrequired)

• Build reusable cohort pipelines

• Optimizequeries for large-scale databases

• Work across multiple CDMs (OMOP, Sentinel,PCORnet)

Core Competencies

• Analytical rigor and methodological depth

• Cross-functional collaboration

• Ability tooperatewith agility across diverse projects and therapeutic areas

• Clear and effective scientific communication