Healthcare Research Scientist

Joint Commission

47d•$106,000 - $138,000

About The Position

Joint Commission seeks a Healthcare Research Scientist with deep expertise in causal inference and healthcare data analytics to advance its enterprise-wide research and improvement agenda. The role applies rigorous statistical methods to observational data, including electronic health records and administrative claims, to generate actionable insights supporting health system improvement, quality improvement strategy, and public accountability. The position manages research partnerships and ensures externally communicated findings align with Joint Commission’s strategic goals and mission. It collaborates across certification, accreditation, and performance measurement programs to extend the organization’s analytic capabilities and promote quality in healthcare delivery.

Requirements

PhD or equivalent in economics, health services research, epidemiology, biostatistics, public policy, or a related quantitative field.
At least 2 years of postdoctoral experience applying causal inference methods to real-world healthcare data, especially administrative claims.
Peer-reviewed publication record in 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 designing and overseeing analytic projects from concept through execution, including managing scope, timelines, and outputs in research or applied settings.

Nice To Haves

Experience with Joint Commission quality process and outcome measures.
Experience conducting research with or within an organization where outputs must align with institutional mission and communications strategy, such as ASPE, CMS, AHRQ, VA, state agencies, or consulting companies.
Familiarity with predictive modeling or statistical learning methods, especially when integrated with causal inference approaches and natural language processing.
Familiarity with regulatory, payer, or policy contexts, such as CMS Conditions of Participation, state Medicaid programs, or alternative payment models.
Experience working in cross-functional teams, collaborating across roles, and managing project-based responsibilities without direct supervisory authority.

Responsibilities

Design and lead applied research studies estimating associations and causal effects from non-randomized healthcare data, contributing to priorities in quality measurement, accreditation and certification evaluation, and system-level learning.
Develop and implement advanced analytic methods, including instrumental variables, difference-in-differences, regression discontinuities, marginal structural models, propensity score-based techniques, synthetic controls, and machine learning.
Work with Joint Commission data resources, including accreditation survey findings and patient-level data from participating health systems, and external sources such as Medicare and Medicaid claims.
Build relationships and consensus across operational, clinical, and business functions to support research that advances Joint Commission priorities.
Translate research findings into accessible, policy-relevant insights for business leaders, regulators, hospitals, and the public.
Ensure public-facing research outputs and communication reflect the strategic and reputational interests of 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 Joint Commission, National Quality Forum, and external partner teams to inform accreditation standards, quality improvement engagement, benchmarking, and outcomes-driven certification.
Provide direction to and oversee data analysts and research assistants supporting project work, ensuring high-quality and timely analytic execution.
Contribute to a culture of rigor, transparency, and equity in research planning, execution, and dissemination.