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 modelling 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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