Data Scientist Drug Repurposing

Job Title Data Scientist Drug Repurposing Function / Department Data Science / AI Employment Type Full-Time, Permanent Location On-site POSITION OVERVIEW The Data Scientist - Drug Repurposing will design and apply Natural Language Processing (NLP), graph-based, and deep learning models to identify new therapeutic applications for existing drugs. Working within a multidisciplinary team of researchers, data scientists, and bioinformaticians, this role will mine large-scale biomedical data including literature, clinical trial records, and multi-omics datasets to surface hidden patterns and translate them into actionable drug repurposing hypotheses. KEY RESPONSIBILITIES NLP for Biomedical Text: Develop and apply NLP pipelines (named entity recognition, relation extraction, literature mining) to extract drug, gene, disease, and pathway information from biomedical literature, clinical trial registries, and electronic health records. Graph-Based Modelling: Design and implement knowledge graphs and graph neural network (GNN) models to represent drug-target-disease interactions and uncover novel repurposing candidates. Deep Learning for Efficacy & Safety Prediction: Build and validate deep learning models to predict drug efficacy, safety signals, and potential new indications from heterogeneous datasets. Multi-Omics Data Integration: Integrate and analyze genomic, proteomic, transcriptomic, and phenotypic data alongside clinical and real-world evidence to identify and priorities repurposing opportunities. Cross-Functional Collaboration: Partner with bioinformaticians, clinical scientists, and product teams to translate model outputs into validated, actionable insights and communicate findings clearly to non-technical stakeholders. Model Lifecycle Ownership: Own model development end-to-end data acquisition, preprocessing, training, validation, deployment, and monitoring with attention to reproducibility and documentation. Staying Current: Track and evaluate emerging methods in NLP, graph learning, deep learning, and computational drug repurposing, recommending adoption where relevant. REQUIREMENTS Education & Experience Bachelor's or Master's degree in Computer Science, Data Science, Computational Biology, or a related field. 23 years of professional experience in Python and applied AI/ML. Technical Skills Strong programming skills in Python (or a similar language). Demonstrated experience applying NLP, graph theory, and deep learning to biomedical data. Hands-on experience with NLP frameworks such as spaCy, NLTK, or transformer-based models (e.g., BERT, GPT). Proficiency with ML/DL frameworks and platforms such as TensorFlow, PyTorch, Keras, or Spark MLlib. Familiarity with graph libraries/frameworks (e.g., NetworkX, PyTorch Geometric, DGL) is an advantage. Core Competencies Excellent problem-solving skills, with the ability to analyze complex, multi-modal datasets and derive actionable insights. Strong written and verbal communication skills, able to convey technical findings to a multidisciplinary, non-technical audience. Proven ability to collaborate effectively within cross-functional, multidisciplinary teams. .