(Senior/Principal) Investigator/Scientist, Data Sc
Posted on: 20/09/2026
Shanghai,Beijing East China
Permanent
Pharmaceutical and Healthcare
We are seeking an experienced scientist to drive novel target discovery through the development and application of AI Agents, bioinformatics, and biomedical data science. This role will build reusable Agent Skills, biomedical knowledge databases, and scalable analytical workflows, and will directly use these capabilities to generate, assess, and prioritize differentiated therapeutic target hypotheses for innovative drug discovery programs.
Key Responsibilities
1. AI-Enabled Target Discovery
• Apply AI agents and computational approaches to identify, assess, and prioritize novel therapeutic targets.
• Build and execute AI-enabled pipelines that produce interpretable target recommendations with traceable evidence, uncertainties, assumptions, and risks.
2. AI Agent, Skill and Platform Development
• Develop an internal AI pipeline platform integrating agents, databases, analytical tools, and reusable workflows for scientific research and target discovery.
• Build AI agents and reusable Skills for question decomposition, evidence retrieval, database querying, tool execution, result validation, evidence synthesis, and report generation.
• Establish evaluation frameworks for scientific accuracy, traceability, reproducibility, robustness, and decision relevance, and continuously improve the platform through real-world projects.
3. Biomedical Data and Knowledge Management
• Contribute to the development and quality improvement of biomedical data and knowledge graphs supporting target discovery.
• Support data integration, entity normalization, ontology mapping, data provenance, quality control, and SQL-based querying and validation.
• Collaborate with data engineering and software teams to improve data accessibility, reliability, and scalability.
Qualifications
• Master’s degree or PhD in Bioinformatics, Computational Biology, Biomedical Informatics, or a related field, with at least three years of relevant experience.
• Experience in therapeutic target identification, assessment, validation, or prioritization, with knowledge of disease biology, translational evidence, and early-stage drug discovery.
• Proficiency in Python or R, with practical experience in SQL, relational data structures, and reproducible workflows.
• Hands-on experience with genetics, transcriptomics, single-cell, proteomics, functional genomics, or other biomedical datasets.
• Strong communication and collaboration skills across scientific, data engineering, and software teams.
Preferred Qualifications
• Experience with LLMs or AI agents, including RAG, tool calling, workflow orchestration, Agent Skills, or output evaluation.
• Experience developing biomedical databases, knowledge graphs, ontologies, or scientific data products.
Key Responsibilities
1. AI-Enabled Target Discovery
• Apply AI agents and computational approaches to identify, assess, and prioritize novel therapeutic targets.
• Build and execute AI-enabled pipelines that produce interpretable target recommendations with traceable evidence, uncertainties, assumptions, and risks.
2. AI Agent, Skill and Platform Development
• Develop an internal AI pipeline platform integrating agents, databases, analytical tools, and reusable workflows for scientific research and target discovery.
• Build AI agents and reusable Skills for question decomposition, evidence retrieval, database querying, tool execution, result validation, evidence synthesis, and report generation.
• Establish evaluation frameworks for scientific accuracy, traceability, reproducibility, robustness, and decision relevance, and continuously improve the platform through real-world projects.
3. Biomedical Data and Knowledge Management
• Contribute to the development and quality improvement of biomedical data and knowledge graphs supporting target discovery.
• Support data integration, entity normalization, ontology mapping, data provenance, quality control, and SQL-based querying and validation.
• Collaborate with data engineering and software teams to improve data accessibility, reliability, and scalability.
Qualifications
• Master’s degree or PhD in Bioinformatics, Computational Biology, Biomedical Informatics, or a related field, with at least three years of relevant experience.
• Experience in therapeutic target identification, assessment, validation, or prioritization, with knowledge of disease biology, translational evidence, and early-stage drug discovery.
• Proficiency in Python or R, with practical experience in SQL, relational data structures, and reproducible workflows.
• Hands-on experience with genetics, transcriptomics, single-cell, proteomics, functional genomics, or other biomedical datasets.
• Strong communication and collaboration skills across scientific, data engineering, and software teams.
Preferred Qualifications
• Experience with LLMs or AI agents, including RAG, tool calling, workflow orchestration, Agent Skills, or output evaluation.
• Experience developing biomedical databases, knowledge graphs, ontologies, or scientific data products.