At Bristol Myers Squibb, we are inspired by a single vision – transforming patients’ lives through science. In oncology, hematology, immunology and cardiovascular disease – and one of the most diverse and promising pipelines in the industry – each of our passionate colleagues contribute to innovations that drive meaningful change. We bring a human touch to every treatment we pioneer. Join us and make a difference. CITRE, based in Seville, Spain, is Bristol-Myers Squibb's research institute in Europe, and link to the European research community. Informatics & Predictive Sciences at CITRE performs innovative computational research to inform decisions across all stages of drug development.
Areas of research include computational and network biology, machine/deep learning, cheminformatics, predictive modeling, patient stratification, and method development for analysis and interpretation of biological and real-world data. In close collaboration with our in-house laboratory, we look to set a new standard in rational design of novel targeted therapies for well-defined patient segments. We are looking for an enthusiastic, creative and collaborative Research Scientist, Applied Machine Learning to join a multi-disciplinary team to: Develop and apply deep learning models to characterize disease biology in large, single-cell transcriptomic, (epi)genomic, and proteomic datasets Implement and evaluate novel methods to extract and interpret critical features from high-content, experimental data Develop generative and predictive approaches to scientific problems in drug discovery Build robust and efficient solutions using on-site or cloud-based infrastructure Collaborate with computational and experimental scientists to derive and validate insights of therapeutic relevance Requirements: PhD in a relevant discipline, accompanied by original research publications Experience incorporating modern deep learning concepts (e.g. attention, graph-based, disentanglement) into models applied towards real-world challenges Strong grasp of scientific programming languages (e.g. Python, R) and relevant libraries and software (e.g. PyTorch, TensorFlow). Demonstrated ability to clearly communicate technical concepts to audiences with diverse scientific backgrounds Previous experience using cloud-based computing and software engineering frameworks (e.g. Docker, Git) Verbal and written English language fluency. Please, attach a cover letter with your motivation for applying to this role, this would be highly appreciated in order to consider your profile for this opportunity. The position advertised is fixed-term (24 months) and based in Seville, Spain. These positions present a unique opportunity to experience research in an industry setting and to contribute to helping patients with unmet medical need, while performing cutting-edge research and maintaining close ties to academia. We are proud of being awarded in Spain as a Top Employer company with certified excellence in employee conditions and also of being recognized by Spanish Government with the Equality Seal (DIE) due to our commitment with diversity, equality and inclusion. Furthermore, we are very happy of also being recognized in Portugal where we have been certified as a Great Place to Work. Bristol Myers Squibb is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to gender identity, race, colour, religion, sexual orientation, national origin or disability. Around the world, we are passionate about making an impact on the lives of patients with serious diseases. Empowered to apply our individual talents and diverse perspectives in an inclusive culture, our shared values of passion, innovation, urgency, accountability, inclusion and integrity bring out the highest potential of each of our colleagues. Bristol Myers Squibb recognizes the importance of balance and flexibility in our work environment. We offer a wide variety of competitive benefits, services and programs that provide our employees with the resources to pursue their goals, both at work and in their personal lives.