Senior Research Scientist, Health Economic 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 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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