Healthcare Research Scientist

Joint Commission is a healthcare quality and accreditation organization seeking a Healthcare Research Scientist to advance its research and improvement agenda. The role leads causal inference studies using healthcare data, develops advanced statistical analyses, translates findings for diverse stakeholders, and supports quality measurement, accreditation, certification, and system-level improvement.

Responsibilities:

• Design and lead applied research studies that estimate associations and causal effects from non-randomized healthcare data, contribute to Joint Commissions priorities in quality measurement, accreditation and certification evaluation, and system-level learning
• Develop and implement advanced analytic methods, including but not limited to instrumental variables, difference-in-differences, regression discontinuities marginal structural models, propensity score-based techniques, synthetic controls, and machine learning
• Work with JC’s diverse data resources, including accreditation survey findings and patient level data (PLD) from participating health systems, as well as external sources such as Medicare and Medicaid claims
• Build relationships and consensus across operational, clinical, and business functions to support the development, execution, and dissemination of research that advances Joint Commission priorities
• Translate research findings into accessible, policy-relevant insights for diverse audiences including business leaders, regulators, hospitals, and the public
• Ensure that public-facing research outputs and communication reflect the strategic and reputational interests of the Joint Commission
• Publish findings in peer-reviewed journals and contribute to internal strategic products, performance improvement resources, and external stakeholder briefings
• Develop non-technical memos, briefs, and other materials to inform internal leadership decisions and support external stakeholder engagement
• Collaborate with cross-functional teams within the Joint Commission, the National Quality Forum (NQF), and external partners to inform accreditation standards, quality improvement engagement, benchmarking, and outcomes-driven certification
• Provide direction to and oversight of data analysts and research assistants assigned to support project work, ensuring high-quality and timely execution of analytic tasks
• Contribute to a culture of rigor, transparency, and equity in research planning, execution, and dissemination

Requirements:

• • PhD or equivalent in economics, health services research, epidemiology, biostatistics, public policy, or a related quantitative field
• • Peer-reviewed publication record in areas such as healthcare economics, outcomes, delivery, policy, quality, or safety
• • Demonstrated success communicating analytic findings and familiarizing non-researchers with research methods
• • Ability to independently code, execute, and troubleshoot statistical analyses in R, Stata, or Python
• • Commitment to Joint Commission's mission to continuously improve healthcare for the public, in collaboration with key stakeholders
• • Demonstrated track record of designing and overseeing analytic projects from concept through execution, including managing scope, timelines, and outputs in research or applied settings
• • Doctoral-level experience applying causal inference methods to real-world healthcare data, especially Medicare or Medicaid claims preferred
• • Experience with Joint Commission's quality process and outcome measures in prior work
• • Experience conducting research with or within an organization where outputs must align with institutional mission and communications strategy (e.g., ASPE, CMS, AHRQ, VA, state agencies, consulting companies, etc.)
• • Familiarity with predictive modeling or statistical learning methods is welcome, especially when integrated with causal inference approaches and natural language processing (NLP)
• • Familiarity with regulatory, payer, or policy contexts (e.g., CMS Conditions of Participation, state Medicaid programs, or alternative payment models)
• • Experience working in cross-functional teams, including collaborating across roles and managing project-based responsibilities without direct supervisory authority
• • 2 years post-doctoral experience applying causal inference methods to real-world healthcare data, especially administrative CMS claims