Associate Director, Value Analytics

Role Summary

Associate Director, Value Analytics. Remote or Hybrid in AbbVie location. Role is responsible for leading evidence modelling teams to design and execute evidence-synthesis, health economic models. Applies methodological and analytical expertise to predict the value of AbbVie products and develop strategies to demonstrate this to external payer decision makers. Communicate and tailor generated evidence to multiple stakeholder audiences.

Responsibilities

• Develop core global Systematic Literature Reviews (SLR), Indirect Treatment Comparison (ITC) and Health Economic Models (HEM) for HTA, payer and other Access stakeholder assessments, submissions, and negotiations. Support publications associated with these to meet business objectives.
• Providing strategic, methodological, and analytical support to affiliates with adaptation of the Global Models (ITC, HEM) for local HTA submissions and support the local assessment of HTA strategy. Coordinate data requirements across biostatistics and evidence generations functions (Clinical Development; HEOR and Medical Affairs) to support Pricing & Reimbursement
• Lead evidence modelling teams (cross-functional and multi countries) to source affiliate and other stakeholder needs, develop strategic options and co-create solutions. Tracking progress, motivating, and leading peers in a matrix team.
• Core member of VAT when leading modelling teams contributing to development of integrated access strategy and attend other cross-functional teams as required.
• Identify need for preliminary ITCs and HEMs to inform value proposition, development, pricing and commercial strategic forecast and decisions.
• Lead and facilitate the sharing of best practices and key learnings across affiliates and cross-functional partners.
• Use analytical techniques leveraging available clinical data and published information to inform pricing and market access strategy.
• Collaborate with the Data and Statistical Sciences (DSS) team for trial analyses, HTA challenges, and statistical analyses of clinical trial data.
• Act as a Strategic Partner for key partners from V&E (HEOR Strategy, RWE Analytics) and iMAP (HTA Strategy, Pricing) to ensure alignment to brand and commercial, access and evidence strategy. Ensure optimal utilization of modelling evidence through partnership with teams generating payer & promotional materials, generating regulatory dossiers and publications.
• Provide mentoring and coaching support to junior members of evidence modelling teams and across function.

Qualifications

• Advanced degree MS/MSc/Mphil/MPH (required) or PhD (preferred) in Health Economics, Epidemiology, Statistics, Biostatistics, Public Health, Econometrics, or other related field.
• At least 8 years relevant experience in consultancy or pharmaceutical industry in Evidence-synthesis, Health Economics, Health Technology Assessment, or related area
• Excellent oral and written communication skills, and strong interpersonal and leadership skills
• Expert knowledge of both health economics and evidence-synthesis disciplines as relevant to HTA, pricing, reimbursement, and value assessment
• Ability to leverage deep expertise to design custom solutions for diverse stakeholder groups.
• Demonstrated leadership in a global matrix environment with multiple stakeholders.
• Proven experience interacting with and/or delivering submissions to HTA agencies, or other payer or decision-making bodies.
• Skills and experience within one of the core capabilities: Health economics, Economic modelling and budget impact analysis. Working hands-on knowledge of Excel for economic models and/or BIMs including VBA
• Skills and experience within one of the core capabilities: Evidence-synthesis Designing SLR search strategies, feasibility analysis for Indirect comparisons. Critically appraise reporting of SLRs and ITC/NMAs
• Skills and experience within one of the core capabilities: Statistics HTA-relevant statistical methodologies, for example indirect treatment comparisons, survival analysis, or utility estimation. Working knowledge of statistical and data processing software (R preferred)

Education

• MS/MSc/MPhil/MPH required or PhD preferred in a related field as listed above.