Celgene is a global biopharmaceutical company leading the way in medical innovation to help patients live longer, better lives.
Our purpose as a company is to discover and develop therapies that will change the course of human health. We value our passion for patients, quest for innovation, spirit of independence and love of challenge.
With a presence in more than 70 countries, and growing - we look for talented people to grow our business, advance our science and contribute to our unique culture.
Research Analytics is a global team of computational scientists, operating across six R&D sites. We pursue innovative computational research towards Celgene objectives, across domains ranging from bioinformatics, computational biology to machine learning, systems biology and mathematical modeling.
Current areas of investigation include application and development of novel computational approaches to unravel the mechanism of action of Celgene’s pipeline compounds and identify therapeutic targets, methods for integrative analysis of omics data, predictive approaches to patient stratification, and mathematical models of intra-
and inter-cellular processes. In addition, we are involved in a range of academic collaborations aimed at understanding disease at molecular and systems level.
To join our team at the Celgene Institute for Translational Research Europe (CITRE) in Seville, Spain, we are looking for a Research Scientist, Machine Learning & Predictive Science.
The successful candidate will work closely with Celgene’s global Research Analytics team to develop and apply robust predictive methodologies across drug development programs.
Patterns identified from high-throughput molecular data will contribute to discovery research, clinical programs, and prediction of patient response across a variety of oncology and inflammatory diseases of unmet medical need.
Also, we encourage our scientists to identify and explore innovative ways for computational research to impact projects and guide Celgene decision making.
Prior biological or clinical expertise is not required experience of applying machine learning to real-world problems and a strong interest in the interdisciplinary application of predictive methods to life sciences data are imperative.
Responsibilities include, but are not limited to :