Senior Research Scientist/ Health Economics and Decision Modelling

Do you consider yourself a self-starter with a real passion for projects involving innovative methods in health economics and evidence synthesis? Do you love collaborating, moving the ball forward, and rolling up your sleeves? Are you a health economist with a passion for R programming? If so – we want to talk to you! We are growing and seeking a Senior Research Scientist with experience in building health economic models in R to join our Evidence Synthesis and Decision Modelling team. You will collaborate with a highly specialized team of health economists, statisticians and researchers in global, methodologically innovative projects for our pharma and biotech clients. Why join us?

• Innovative Culture: Experience the excitement of a start-up within a well-funded, established global organization.
• Passionate Team: Work with a team that has a real passion for Health Economics and Outcomes Research (HEOR) and prides itself on being visionary leaders in the field.
• Growth Opportunities: Be part of a growing team that values collaboration and making a real difference in healthcare.

About you:

• Experienced Health Economist: You have a background in health economics, statistics or a related field, HEOR experience, and a solid understanding of the pharmaceutical industry and drug reimbursement processes. You can interpret results of clinical and health economic studies independently, and lead the implementation of simulation-based cost-effectiveness analyses in R and Excel/VBA.
• Modelling Research Experience: You have experience planning, programming and reporting R-based and Excel-based cost-effectiveness simulation models and applied knowledge of statistical methods in health economics. You can evaluate studies to identify key result drivers, assess data or methodological gaps and suggest solutions.
• Deliverable Creation: You can program independently economic models in R and Excel/VBA from scratch, and contribute to the development of client-ready study deliverables including model conceptualization and analysis plans, interpretations of model results and sensitivity analyses, and technical reports.
• Technical Proficiency: You demonstrate passion and expertise in R programming applied to health economic simulation modelling, as well as data analysis and visualization. You are proficient with collaborative versioning software (i.e., git/GitHub). You are an expert in Excel and Visual Basic for Applications (VBA) programming. You are skilled in preparing written documentation and presenting results using Microsoft Word and PowerPoint.
• Effective Communication: You can present progress and results clearly to both technical and non-technical audiences, either internally or externally.
• Project and Time Management: You ensure timely delivery of project components, can work effectively individually and as part of a diverse team and have excellent independent organizational and time management skills.

Required Experience and Competencies:

• Master’s degree in health economics or statistics, or a related discipline
• Minimum of 4 years of relevant professional experience, ideally in a consulting environment serving biotech, pharmaceutical or healthcare clients
• Proven R programming skills, experience with programming cost-effectiveness simulation models in R
• Understanding of and experience in the application of statistical methods in health economics, e.g., parametric survival regression model extrapolation, network meta-analyses, probabilistic sensitivity analyses
• Willingness and desire to learn and share knowledge
• Strong multi-tasking and time management skills
• Highly developed analytical reasoning and problem-solving skills
• Ability to work effectively individually and as part of a diverse team

Helpful Experience and Competencies:

• Experience with Python, C++ or other programming languages
• Experience with the hesim, shiny, Rcpp R packages
• Experience with Bayesian statistics and advanced modeling techniques
• Knowledge and experience in conducting indirect treatment comparisons such as network meta-analysis, matching-adjusted indirect comparison, and simulated treatment comparison

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