Bioinformatics Data Scientist

About Us : Our next-generation pharmaceutical client is at the forefront of innovative drug discovery, leveraging cutting-edge technologies to tackle the world’s most pressing health challenges.

They are a multidisciplinary team of scientists, engineers, and bioinformaticians committed to developing transformative therapies.

Join us in our mission to revolutionize healthcare through data-driven insights. Job Overview : We are seeking a highly motivated PhD-level Bioinformatics Data Scientist with expertise in Knowledge Graph Embedding (KGE) model development to support our drug discovery pipeline.

The successful candidate will play a pivotal role in developing and implementing KGE models to identify novel therapeutic targets, drug-repurposing opportunities, and insights into biological pathways.

You will work closely with cross-functional teams, including AI / ML experts, computational biologists, and drug discovery scientists, to accelerate the identification of new treatment possibilities.

Key Responsibilities : Design, develop, and optimize Knowledge Graph Embedding (KGE) models for analyzing biomedical and molecular data in the context of drug discovery.

Integrate and analyze large-scale biological datasets (e.g., omics, chemical, and clinical data) within a knowledge graph framework.

Collaborate with domain experts to generate hypotheses for drug repurposing and target discovery using KGE models.Develop novel algorithms and pipelines to assess the structure and function of biological networks related to disease mechanisms and drug efficacy.

Perform validation and benchmarking of models using public and proprietary datasets.Stay up to date with advances in computational biology, KGE methodologies, and AI / ML applications in life sciences.

Communicate findings and insights to stakeholders through presentations, reports, and scientific publications. Required Qualifications : PhD in Bioinformatics, Computational Biology, Data Science, Machine Learning, or a related field.

Strong expertise in Knowledge Graph Embedding (KGE) models and their applications in life sciences.Proficiency in programming languages such as Python, R, Java, or similar, with experience in libraries / frameworks for KGE (e.

g., PyKEEN, DGL-KE).Deep understanding of biological data sources (e.g., genomics, proteomics, metabolomics) and their integration into knowledge graphs.

Hands-on experience with drug discovery workflows, including target identification and drug repurposing.Strong statistical, machine learning, and deep learning skills, with an ability to interpret complex biological datasets.

Experience working with high-performance computing environments and cloud-based data platforms (AWS, GCP, Azure, etc).Strong communication skills, with the ability to work collaboratively in an interdisciplinary environment.

Preferred Qualifications : Familiarity with graph databases (e.g., Neo4j, RDF stores) and semantic web technologies.Experience in drug discovery or pharmaceutical R&D environments.

A strong publication record in bioinformatics, computational biology, or related fields.Experience with natural language processing (NLP) and text mining in biomedical literature is a plus.

Why Join Us?Opportunity to work on cutting-edge projects with the potential to significantly impact human health.Collaborative and innovative work environment with world-class scientists and technologists.

Competitive salary and comprehensive benefits package.Opportunities for professional development, including attending conferences and publishing your work.

If you are passionate about using cutting-edge bioinformatics and data science techniques to make a difference in drug discovery, we encourage you to apply.

Please apply ASAP if you are interested!