Senior Director Medical Affairs - HEOR

About Us 

Job Overview 

The Senior Director Medical Affairs - Health Economics & Outcomes Research (HEOR) generates and delivers the economic and value evidence behind Kardigan's cardiovascular portfolio. The role is hands-on where it matters most  (directly analyzing real-world cost, utilization, and economic outcomes from claims and linked data), while directing and critically appraising the economic models and evidence synthesis that build on that analysis, and producing the payer-facing value evidence that supports market access from development through the post-launch lifecycle. The position turns value and access priorities into finished deliverables (i.e., cost and resource-use analyses that are run directly, plus health economic models, evidence syntheses, and value dossiers it scopes, directs, and quality-assures) and partners closely with Clinical Development, Commercial, Epidemiology, Data Science / Applied ML, and Business Development. 

Essential Duties and Responsibilities 

Value-Evidence Planning & Prioritization 

• Scope the cost and utilization analyses, economic models, and evidence syntheses each program needs across development and launch, and sequence them against value and access milestones. 

• Prioritize the value and access questions that most affect payer, provider, and health-system decisions, and convert them into concrete analysis and deliverable plans. 

Real-World Cost, Utilization & Outcomes Analysis 

• Personally execute costing, resource-use, and economic-outcomes analyses on claims and linked clinical–claims data, applying unit costs and valuation methods to estimate cost-of-illness, cost consequences, and budget impact for target populations. 

• Analyze utilization and economic outcomes (i.e., hospitalization and 30/90-day readmissions [DRG-level and condition-related], treatment and procedure costs, and pre-/post-procedure care such as AVR/TAVR) to produce the cost inputs, offsets, and economic endpoints that feed models and value claims. 

• Build the direct- and indirect-cost picture from the data, scoping economic and utilization endpoints (and clinical outcomes only insofar as they drive cost). 

Health Economic & Decision-Analytic Modeling 

• Scope, specify, and own the portfolio's economic models (i.e., cost-effectiveness and cost-utility [Markov, microsimulation, discrete-event simulation, or decision-tree], budget-impact, and cost-of-illness) including structure, assumptions, and the value case each model makes. 

• Commission and manage model build by internal modelers or external vendors, and critically appraise, validate, and stress-test results (sensitivity, scenario, and/or value-of-information analysis) before they inform pipeline, pricing, and value decisions. 

• Specify and appraise key model inputs [i.e., treatment effects, health-state utilities and preference weights [e.g., EQ-5D, mapping clinical measures to utilities], costs, resource use, and transition/event probabilities) sourced from trials, literature, real-world data, and Epidemiology-led analyses. 

• Oversee localization of global models for specific payer and reimbursement settings, and ensure models stay transparent, auditable, documented, and defensible to payers and HTA bodies. 

Evidence Synthesis & Relative Effectiveness 

• Direct indirect treatment comparisons, network meta-analyses, and matching-adjusted indirect comparisons (commissioning execution internally or via vendors and critically appraising methods and results) to provide the relative-effectiveness and comparator inputs models require where head-to-head data are missing. 

• Define the comparator and standard-of-care assumptions for models and value claims, drawing on treatment-pattern evidence supplied by Epidemiology. 

• Scope and quality-assure targeted and systematic literature reviews that anchor model parameters and value propositions in the published evidence base. 

Payer Value Evidence & Communication 

• Produce the value deliverables (value propositions, AMCP-format and global value dossiers, payer/HTA evidence packages, and objection-handling materials) that convey clinical and economic value in a strictly non-promotional manner. 

• Support pricing, reimbursement, and formulary work, including post-loss-of-exclusivity pricing research, and build and evaluate the economic case for outcomes-based and value-based contracts. 

• Package economic and outcomes findings into evidence and tools tailored to payers, providers, and health-system decision-makers. 

Data Requirements & Vendor Inputs 

• Specify the HEOR-specific data needed for analyses and models (linked clinical-plus-cost/utilization content [claims, linked EHR–claims, payer/encounter, DRG-level]) with the coverage, linkage, currency, and exportability each analysis requires. 

• Vet candidate datasets and vendors for fit against those requirements, contribute recommendations to the cross-functional sourcing process, and secure analysis-ready data that can be reused across value questions. 

Cross-Functional Delivery & Scientific Communication 

• Deliver in close partnership with Clinical Development, Commercial, Pricing, Epidemiology, Data Science / Applied ML, Business Development, and Finance, and engage Legal, Compliance, Privacy, and Quality to keep economic evidence and data use compliant and non-promotional. 

• Turn analyses into peer-reviewed manuscripts, congress presentations (e.g., ISPOR, AMCP), and value dossiers, and represent the economic evidence with external experts, payers, and health systems. 

Qualifications and Preferred Skills 

Required 

• Doctoral degree in health economics, outcomes research, pharmacoeconomics, health services research, health policy, or a related field (PhD, ScD, DrPH), or a PharmD/MD with HEOR or health-economics fellowship training. 

• 12+ years generating HEOR and value evidence in biopharma, consulting, academic, or comparable settings, including hands-on real-world data analysis of cost, utilization, and economic outcomes. 

• Direct, hands-on experience across the real-world dataset types relevant to cost and utilization (including administrative claims [commercial, Medicare fee-for-service, Medicare Advantage, Managed Medicaid, etc.], linked EHR–claims, hospital cost/charge and encounter [DRG-level] data, and registries) with working command of their respective cost-capture, coverage, and linkage strengths and limitations. 

• Demonstrated hands-on execution of costing, resource-use, and economic-outcomes analyses on claims and linked clinical–claims data with strong proficiency in SAS, R, and/or SQL. 

• Ability to scope, specify, critically appraise, and validate decision-analytic and economic models (cost-effectiveness/cost-utility, budget-impact, and cost-of-illness). 

• Working command of evidence-synthesis and relative-effectiveness methods (indirect treatment comparison, network meta-analysis, matching-adjusted indirect comparison) and of health-state utility and preference valuation. 

• Fluency in market access, payer, HTA, reimbursement, and value-communication expectations (e.g., AMCP dossiers, ISPOR good-practice standards), including outcomes-based contracting. 

• Grasp of data privacy and compliance (e.g., HIPAA) and of the non-promotional rules governing economic and value evidence. 
• A record of delivering through matrixed, cross-functional teams. 

Preferred 

• Cardiovascular therapeutic-area background (e.g., heart failure, hypertension and hypertensive crisis, cardiomyopathy/DCM, valvular and aortic stenosis). 

• Hands-on economic-model building experience (e.g., Excel, R, TreeAge). 

• Experience folding economic and resource-use endpoints into clinical trials and turning trial data into value evidence. 

• Exposure to global HTA submissions and payer systems (e.g., NICE, HAS) alongside US payers and integrated delivery networks. 

• Hands-on design or evaluation of value-based and outcomes-based contracts. 

• Early-stage and pipeline economic modeling that informs portfolio prioritization and business-development decisions. 

• Experience selecting and managing external data vendors and analytics partners and assembling reusable value-evidence assets. 

• A solid publication and congress-presentation record in HEOR, health economics, or outcomes research. 

• Comfort delivering in a matrixed setting.