Healthcare Data Scientist: Causal Inference & Impact

Joint Commission; United States; On-site; USD 106,000 - 138,000; Full time

Benefits offered by this job

Benefits package

Job summary

Joint Commission seeks a Healthcare Research Scientist with deep expertise in causal inference and healthcare data analytics to advance our enterprise-wide research and improvement agenda.

The ideal candidate will have demonstrated success in applying rigorous statistical methods to observational data, including electronic health records (EHRs) and administrative claims, to generate actionable insights that support health system improvement, quality improvement strategy, and public

Qualifications

• PhD or equivalent in economics, health services research, epidemiology, biostatistics, public policy, or a related quantitative field.
• 2 years post-doctoral experience applying causal inference methods to real-world healthcare data, especially administrative claims.
• 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.

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.

Skills

Causal inference

Healthcare data analytics

Communication of analytic findings

Public policy

Education

PhD in economics, health services research, epidemiology, biostatistics, public policy, or related

Tools

R

Stata

Python

Job description

Joint Commission seeks a Healthcare Research Scientist with deep expertise in causal inference and healthcare data analytics to advance our enterprise-wide research and improvement agenda.

The ideal candidate will have demonstrated success in applying rigorous statistical methods to observational data, including electronic health records (EHRs) and administrative claims, to generate actionable insights that support health system improvement, quality improvement strategy, and public