We are seeking a highly skilled and motivated Biostatistician to support our Research and Development team in evaluation of clinical retrospective outcome data for improving immunologic risk assessment in transplantation. The ideal candidate will have a strong foundation in statistics with 3+ years of hands-on experience working with clinical data and is located in Berlin (Germany) or Utrecht (Netherlands).
Tasks
* Passionate about data and understanding its potential impact on patients
* Skilled in statistical methods such as survival analyses, regression analyses, linear models, significance tests
* Expert in R, proficient in Python
* Familiarity with common R data science libraries(e.g. tidyr, ggplot, survival)
* Familiarity with machine learning models such as neural networks, random forests or support vector machines
* Familiarity with Github and collaborative development
* Full professional proficiency in English language
* Comfort and adaptability in working with new and emerging technologies
* Effective at executing independent and group tasks in a self-organized way
Requirements
* MSc in mathematics, biostatistics, computational biology or equivalent
* 3+ years hands-on experience in clinical research
* Experience working in a scientific environment
* Familiarity with immunology in HLA and transplantation is a plus
* Experience with artificial intelligence algorithms is a plus
Benefits
In addition to a competitive salary package, we offer a range of comprehensive benefits tailored to the specific needs of our team members. We understand the importance of work-life balance and the unique considerations that come with different regions.
You will play a crucial role in evaluating existing and novel prediction pipelines in the context of transplantation outcome data to aid in clinical risk assessment. Your key responsibilities will include:
* Collaborating with external partners on their clinical datasets
* Identifying and implementing appropriate and content-specific statistical models
* Data preprocessing and cleaning for statistical analyses
* Assessing results of prediction models
* Actively participating in agile development processes, advocating for best practices and contributing to continuous improvement initiatives.