Systematic Review Scientist - Schaumburg, IL; Washington, DC; or Remote

The Systematic Review Scientist will lead and support high-quality evidence synthesis projects, with a strong emphasis on meta-analyses and network meta-analyses. This role requires deep methodological expertise combined with a forward-looking mindset. The successful candidate will be comfortable leveraging artificial intelligence (AI) and automation tools to increase efficiency, consistency, and scalability across the evidence synthesis lifecycle.

• Design, conduct, and report systematic reviews in accordance with methodological best practices (e.g., PRISMA, Cochrane, GRADE).

• Perform pairwise meta-analyses and network meta-analyses, including model selection, assessment of heterogeneity, inconsistency, and sensitivity analyses.

• Develop and execute reproducible workflows for literature searching, screening, data extraction, and risk-of-bias assessment.

• Lead or contribute to protocol development, statistical analysis plans, and final reports/manuscripts.

• Collaborate with clinical experts, statisticians, and stakeholders to ensure methodological rigor and relevance.

• Apply AI-enabled tools (e.g., machine-learning-assisted screening, data extraction, LLM-based summarization) to streamline review processes while maintaining quality and transparency.

• Evaluate, validate, and refine AI workflows to ensure appropriate use, auditability, and methodological integrity.

• Stay current with advances in evidence synthesis methods, statistical approaches, and AI applications.

• Mentor less experienced staff and contribute to internal methodological standards and guidance, as appropriate.

• Provide support, project management, subject-matter expertise and liaison services to assigned Committees and/or Councils.

• Bachelor's degree in related field required. Master's degree in Public Health, Epidemiology, Statistics, Health Economics, or related discipline preferred.

• Demonstrated experience conducting systematic reviews and meta-analyses including hands-on experience with network meta-analysis. Published peer-reviewed systematic reviews and/or network meta-analysis is preferred.

• Strong understanding of evidence synthesis standards, risk-of-bias tools, and reporting guidelines.

• Experienced user of Microsoft Office products: Word, Excel, and PowerPoint.

• Proficiency with statistical software used for meta-analysis (i.e., R, Stata, SAS, or equivalent).

• Experience using reference management and systematic review platforms (i.e., EndNote, Covidence, DistillerSR, Rayyan, or similar.

• Familiarity with reproducible research practices (i.e., version control, reproducible code) is preferred.

• Experience working with guideline development, HTA, or regulatory contexts is preferred.

• Practical experience using AI or machine-learning-enabled tools to support systematic review tasks (i.e., screening, data extraction, summarization, quality checks) is preferred.

• Ability to design efficient, scalable workflows that integrate AI while preserving methodological rigor and transparency is preferred.

This position is an exempt/salaried position. The target hiring pay range for this position is: $115,000 to $145,000 annually. This position is not eligible for a yearly bonus/incentive.