Senior Advisor - Payment Model Evaluation

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Senior Advisor – Payment Model Evaluation based in the United States.

We are seeking a highly experienced healthcare policy and evaluation expert to provide strategic guidance on the design and evaluation of healthcare payment models. In this role, you will contribute to the development of rigorous, evidence-based evaluation strategies that align with policy objectives and statutory requirements. You will leverage advanced quantitative methods, claims data analysis, and economic modeling to assess healthcare costs, quality outcomes, and target populations. Working closely with policy specialists and evaluation teams, you will help bridge the gap between policy formulation, payment model design, and analytical rigor. Your expertise will support informed decision-making and strengthen the methodological quality of healthcare innovation initiatives. This fully remote, part-time opportunity offers the chance to contribute to meaningful healthcare policy projects while collaborating with multidisciplinary experts. The assignment is expected to last eight months or longer, with a flexible commitment of 10–20 hours per week.

Accountabilities

Provide high-level subject matter expertise on payment model evaluation and design, ensuring evaluation approaches align with policy objectives, program requirements, and applicable statutory provisions.
Advise project teams on the development of methodologically rigorous evaluation frameworks that support reliable measurement of healthcare payment model performance and impact.
Conduct and guide quantitative analyses using healthcare claims data to assess condition prevalence, refine target population definitions, and identify relevant evaluation parameters.
Analyze healthcare costs and quality measures to inform payment model development, assess potential outcomes, and strengthen evidence-based policy decisions.
Perform and interpret statistical power calculations to determine appropriate evaluation designs, assess feasibility, and support robust measurement of model effectiveness.
Collaborate with policy, analytics, and evaluation teams to ensure proposed study designs meet methodological rigor, quality, and certification expectations.
Bridge policy development, payment model design, and evaluation methodology by advising stakeholders on trade-offs between policy objectives, operational feasibility, and analytical requirements.
Provide expert recommendations on causal inference approaches, econometric methods, measurement strategies, and study design considerations.
Participate in project meetings and technical discussions, offering substantive input on evaluation strategies, methodological decisions, implementation considerations, and analytical plans.
Translate complex quantitative findings and methodological considerations into clear, actionable recommendations for multidisciplinary teams and decision-makers.
Support the development and refinement of evaluation plans that promote credible findings, sound policy decisions, and effective assessment of healthcare payment initiatives.
Requirements
A Ph.D. in Health Policy, Public Policy, Health Services Research, Economics, Advanced Quantitative Methods, or a related discipline.
At least 10 years of relevant professional experience in healthcare claims data analysis, causal inference, econometrics, healthcare cost measurement, and quantitative study design.
In-depth knowledge of Medicare or Medicaid payment policies, including the policy considerations that influence healthcare payment model development and evaluation.
Advanced expertise in quantitative research methodologies, including causal inference techniques, econometric analysis, statistical modeling, and evaluation design.
Demonstrated experience analyzing healthcare claims data to evaluate healthcare utilization, condition prevalence, cost patterns, quality outcomes, and patient populations.
Strong understanding of statistical power calculations, population definition methodologies, and the design of rigorous evaluations for healthcare policy and payment initiatives.
Proven ability to provide strategic, evidence-based guidance on complex evaluation designs and methodological decisions.
Ability to assess competing priorities and explain the trade-offs between policy objectives, model design, analytical feasibility, and evaluation rigor.
Excellent analytical and problem-solving skills, with the ability to interpret complex data and translate findings into practical recommendations.
Strong written and verbal communication skills, including the ability to explain technical concepts clearly to policy experts, researchers, analysts, and other stakeholders.
Demonstrated collaboration skills and the ability to work effectively with multidisciplinary teams in a remote consulting environment.
Strong attention to detail, sound professional judgment, and a commitment to methodological integrity, evidence-based decision-making, and high-quality research.
Benefits
Competitive hourly compensation: $89–$95 per hour, depending on qualifications and relevant experience.
Fully remote work: Perform your responsibilities remotely from the United States.
Part-time schedule: Flexible workload of approximately 10–20 hours per week.
Extended project engagement: Temporary assignment expected to last eight months or longer.
Meaningful healthcare policy impact: Contribute expertise to the evaluation and design of healthcare payment models supporting public healthcare innovation initiatives.
Advanced analytical work: Apply specialized knowledge of econometrics, causal inference, claims data, and evaluation methodology to complex policy challenges.
Multidisciplinary collaboration: Work alongside policy, research, and evaluation professionals to strengthen evidence-based decision-making.
Expert-level contribution: Influence evaluation strategies, methodological approaches, and policy-related decisions through specialized subject matter expertise.