The University Medical Center Utrecht's Department of Data Science and Biostatistics is looking for a senior-level professional with skills in data science, software engineering, and statistical programming to develop federated analytical pipelines. These pipelines will be used to process large amounts of digital healthcare data from routine patient care into real-world evidence, allowing amongst others, studies on the safety and effectiveness of vaccines and medicines.

The successful candidate is a skilled and enthusiastic programmer with a MSc or PhD degree in (Bio)statistics, Informatics, Data science, Econometrics, Epidemiology, Health Sciences or a related field, and has a broad interest in medical research. You have demonstrated experience in use of R, SQL and GitHub. JAVA, Python, Shiny and "Unit tests" experience are an advantage. Fluency in English, both verbal and in writing, is required, as well as proficiency in data management /engineering and quality management of programming.

You are an analytical person who has ample experience in programming and data management / engineering, in a multidisciplinary setting and embraces open science. Quality and accountability are important for you. You are able to deliver on time and a team player.

The maximum salary for this position (36 - 36 hours) is € 6.156,00 gross per month based on full-time employment.

In addition, we offer an annual benefit of 8.3%, holiday allowance, travel expenses and career opportunities. The terms of employment are in accordance with the Cao University Medical Centers (UMC).

The Julius Center for Health Sciences and Primary Care is one of the 10 divisions within the UMC Utrecht. The Julius Center aims for a leading role in the acquisition and dissemination of knowledge in the field of Health Sciences and Primary Care. Through the years, the Julius Center has distinguished itself through high quality scientific research and education. The department of Data science & Biostatistics focuses its research and main projects on Data Science, Clinical trials methodology, Real World Evidence and Methodology for high dimensional (big) data. It has an academic staff of about 40 statisticians, epidemiologists, and data scientists and 20 data managers. The Real-World-Evidence group in the department of Datascience & Biostatistics focuses on transformation of distributed big routine health data into evidence that can be used by medicines regulators (e.g. European Medicines Agency) and health care professionals as part of a learning health ecosystem such as Health RI.