Hiring: Healthcare Research Scientist Company: Joint Commission Location: United States Job Posted Time: 2026-09-03 11:18:34 Target Skills & Keywords : Electronic Health Records, Machine Learning, Medicaid, Medicare, NLP, Python, R About the job Experience: •2 years post-doctoral experience applying causal inference methods to real-world healthcare data, especially administrative CMS claims. Required Skills: •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. •Interface directly 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. Qualifications: •PhD or equivalent in economics, health services research, epidemiology, biostatistics, public policy, or a related quantitative field. •Doctoral-level experience applying causal inference methods to real-world healthcare data, especially Medicare or Medicaid claims preferred. •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. •Demonstrated capacity 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. •Operational familiarity with predictive modeling or statistical learning methods is welcome, especially when integrated with causal inference approaches and natural language processing (NLP). •Operational familiarity with regulatory, payer, or policy contexts (e.g., CMS Conditions of Participation, state Medicaid programs, or alternative payment models). Compensation: •Contribute to a culture of rigor, transparency, and equity in research planning, execution, and dissemination Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!