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 our 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 data.
Position
Biomedical knowledge discovery and data mining refers to the research area focusing on developing methodologies to extract useful biomedical associations, patterns, rules from huge amount of heterogeneous data, including but not limited to biomedical knowledge graph, various databases, and scientific literature.
We seek an innovative scientist specialized in knowledge discovery and data mining with experience in the Biomedical domain to join the Department of Informatics & Predictive Sciences. This individual will contribute to our growing efforts to develop/apply cutting-edge AI/ML, natural language processing (NLP), and graph machine learning techniques to uncover hidden but critical information from heterogeneous data stored in unstructured, semi-structured, and structured formats. The position offers the great opportunity to make big impact to Bristol Myers Squibb.
The successful candidate will pursue key research objectives, including development and implementation of text/data mining and machine learning algorithms to discover potential business development (BD) opportunities, and development of novel graph machine learning algorithms over very large knowledge graph to assist the internal drug discovery and development research.
This individual will work alongside a multidisciplinary team of computer scientists, data scientists, wet-lab and biological domain experts. The role offers unprecedented opportunity to directly impact the origination and delivery of transformational and life-changing therapies in key diseases of unmet medical need. We encourage inquiries from those with a strong background in AI/ML/NLP/graph machine learning or who also have a strong interest in innovation and interdisciplinary application of computational approaches to life sciences data.
Responsibilities
- Develop and apply advanced text/data mining and AI/ML techniques to conduct research and deep analysis over relevant biotech landscape.
- Participate in research projects involving the development of cutting-edge graph machine learning algorithms over very large knowledge graphs for the internal drug discovery and development research.
- Collaborate as a member of cross-functional teams to design experiments, guide data generation, and validate in silico findings.
- Author scientific reports and present methods, results, and conclusions to publishable standard
Qualifications & Experience
- Ph.D. with AI/ML/NLP/graph machine learning focus in Computer Science, Data Science, Biomedical Informatics, or a related field with a firm grasp of quantitative methods.
- 2+ years of industry or postdoc working experience in conducting machine learning and data science research (preferably in the biomedical settings). Candidates with an extensive AI/ML background and no prior experience in biomedical/biology are also encouraged to apply.
- Strong experience in developing supervised and unsupervised machine/deep learning methods, preferably with strong experience in the deep graph neural network and graph embeddings approaches.
- Strong expertise in scientific programming languages (e.g., Python), libraries (e.g., PyTorch, Tensorflow), graph machine learning packages, graph databases, and training/deploying models in a cloud computing environment (preferably with AWS).
- Experience in building time-series predictive model.
- Experience in applying NLP/text mining/data mining techniques over large corpus.
- Experience with web-based application development and data visualization (e.g., D3.js, R Shiny).
- Familiarity with version control services such as GitHub or GitLab. Please include a link to your public repository in the cover letter.
- Problem-solving skills, adaptability and collaborative nature.
- Excellent verbal and written communication skills.
- Fluent English language fluency are prerequisite.
