Data Scientist Drug Repurposing (Ahmedabad)

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 - Solid 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. .