Job Description

The Role

What You'll Own

Study design and governance

Scope studies of the deployed platform: define the question, cohort, observation period, and endpoints from operational data

Choose and defend designs where randomization is not available: retrospective observational, matched cohort, difference-in-differences, interrupted time series, index-event designs

Build comparison groups, baselines, and risk-adjustment approaches that hold up under external scrutiny

Write prespecified analysis plans and decide up front what a study will and will not claim

Own IRB submission and human-subjects determination, and conflict-of-interest and AI-assistance disclosure

Shape how future deployments collect data so they are publishable by design

Analysis

Analyze Medicare claims, EHR, platform, and customer operational data

Apply matching and causal inference methods: propensity score and Mahalanobis matching, coarsened exact matching, mixed-effects models for facility-clustered data

Reason about confounding, selection bias, immortal time bias, and balance diagnostics, and argue why a cohort or comparator is defensible

Translate how Circadia operates and bills into claims-detectable logic (NPIs, place of service, RPM/RTM CPT codes, type-of-bill logic, Part A/B interaction, dual eligibility, benefit exhaustion, MDS)

Define how Circadia calculates and reports ROI and cost avoidance, and run subgroup analysis to identify where the platform delivers the strongest outcomes

Publication and dissemination

Draft and publish manuscripts in journals such as JAMDA and JAMIA, and manage peer review and revision

Produce conference abstracts, white papers, and commercial evidence from the same analysis, without letting claims drift between versions

Set the guardrails on what Circadia can and cannot claim from its evidence

Present findings to clinical, payer, and executive audiences, and support customer and partner conversations

What We're Looking For

Industry research experience is required. This role is not a fit for candidates whose experience is entirely academic
MSc or PhD in epidemiology, health services research, biostatistics, health economics, public health, or a related quantitative field
5+ years producing real-world evidence, outcomes research, or health economics work in a company, consultancy, payer, or provider setting
Experience with health economics and outcomes research: cost, utilization, ROI, or cost-avoidance analysis alongside clinical endpoints
Ability to translate technical findings into clear narratives for executives, customers, and payers
Track record of peer-reviewed publication, ideally in a clinical specialty or informatics journal
Command of observational and quasi-experimental study design, and clear thinking about its limits
Proficiency with R or Python and SQL, and comfort with large, messy healthcare datasets
Clear scientific writing, and experience carrying a manuscript through peer review

Especially Valuable

Experience with Medicare, Medicare Advantage, skilled nursing, or senior care populations
Working knowledge of Medicare claims structure and CMS post-acute quality measures (SNFRM, PPR)
Experience in remote patient monitoring, medical devices, or digital health
Experience evaluating AI or machine learning models in a clinical workflow
Experience measuring clinician workload, documentation burden, or workflow efficiency, including clinical informatics evaluation
Experience in a startup
Familiarity with IRB pathways and reporting standards such as SQUIRE 2.0
Experience working with actuarial firms or third-party claims datasets
Understanding of value-based care economics and healthcare reimbursement

Work Environment