Head of AI-aided Drug Discovery - Innovent Biologics : Job Details

Position Summary

This position will formulate and execute the department's comprehensive strategy, ensuring the deep integration of cutting-edge artificial intelligence and machine learning technologies into the early discovery pipelines for both small molecules and biologics. The Head will manage and develop a team of top-tier scientists, drive technological innovation, and hold key accountability for pipeline project progression and candidate delivery.

Key Responsibilities

I. Strategic Planning & Departmental Leadership

• Formulate Company-wide AIDD Strategy: As the overall head of AIDD, formulate and execute a company-wide long-term strategy, technology roadmap, and implementation blueprint to establish AIDD as a key pillar of the company's core R&D competency.
• Lead & Develop the Team: Build from scratch and continuously develop a world-class AIDD team. Establish a clear organizational structure, talent pipeline, and career development pathways to foster a culture of excellence, innovation, and collaboration.
• Resource & Budget Management: Manage the department's annual budget planning, resource allocation, and operational management to ensure that investments maximally support strategic goals and pipeline needs, achieving a high return on investment.

II. Technology Foresight & Platform Development

• Track & Evaluate Emerging Technologies: Continuously track disruptive technologies and the latest trends in the global AIDD field (academia and industry), such as foundation models, multi-modal AI, and quantum computing. Conduct forward-looking assessments and provide expert recommendations to R&D leadership to ensure the company's technological competitiveness.
• Drive Internal Technology Innovation: Lead high-risk, high-reward internal exploratory research projects. Drive the internal incubation and validation of new algorithms and models to build a proprietary technology portfolio and rapidly translate cutting-edge research into productive capabilities.
• Guide Infrastructure Development: Guide and oversee the planning and development of the company-wide AIDD computational platform, data strategy, and MLOps ecosystem. Ensure robust data governance (FAIR principles) and efficient, scalable processes for model development, deployment, and iteration.

III. Pipeline Driving & Project Impact

• Deeply Engage in Portfolio Decision-Making: Actively participate in the management of the early R&D portfolio, providing decisive data-driven support and scientific judgment on project initiation, progression strategy, risk assessment, and resource allocation from an AIDD perspective.
• Accelerate Candidate Delivery: Ensure the AIDD team is deeply embedded as a core member in all discovery projects, sharing accountability with experimental teams to tangibly accelerate the progression from target validation and hit finding to Preclinical Candidate (PCC) nomination through efficient computational design and data analysis.
• Quantify Performance & Contribution: Establish and execute a quantitative evaluation system (KPIs) to measure the specific contributions and return on investment (ROI) of the AIDD department to pipeline projects, such as reduced cycle times, increased success rates, and quality of designed molecules.

IV. Organizational Influence & Collaboration

• Strengthen Cross-Functional Collaboration: As a key member of the R&D leadership team, establish effective strategic partnerships with the heads of Medicinal Chemistry, Biologics, Biology, DMPK, and Translational Medicine to break down silos and promote a truly integrated wet-lab/dry-lab R&D model.
• Promote AI Literacy Across the Organization: Champion and foster a culture of data-driven decision-making throughout the R&D organization. Increase the AI literacy and capabilities of all R&D personnel through training, seminars, and other initiatives.
• Build an External Collaboration Ecosystem: Represent the company in establishing and maintaining strategic collaborations with top-tier academic institutions, AI technology companies, and biotech partners to integrate external intelligence and resources into an open innovation ecosystem.

Basic Qualifications

• Ph.D. in machine learning，Computational Chemistry, Computational Biology, Computer Science, or a related field.
• 10+ years of drug discovery experience in the pharmaceutical or biotechnology industry.
• 5+ years of experience in a team management or departmental leadership role with a proven track record of successful team building.

Preferred Qualifications I. Strategic Project & Pipeline Accomplishments

• Exceptional Track Record: A distinguished record of playing a key leadership role in drug discovery, having successfully advanced multiple drug candidates (ideally covering both small molecules and biologics) from early discovery to IND-enabling studies or clinical development, where AIDD strategies played a decisive role.
• Team Building Experience: Verifiable success in building, scaling, and leading high-performing AIDD or computational science teams from an early stage.
• Strategic Influence: A history of successfully influencing R&D portfolio strategy and key project Go/No-Go decisions through data-driven, AI-powered insights.

II. AIDD Technical Strategy & Depth

• AI in Target Discovery & Assessment: Deep experience in applying AI and ML models for novel target identification and druggability assessment, for example, by building predictive models from multi-modal data (multi-omics, clinical, literature) for more accurate target prioritization.
• Expertise in Deploying Cutting-Edge AI: A deep strategic understanding and deployment experience with cutting-edge AI technologies across the entire discovery workflow, including but not limited to: Generative Models for molecule generation, Graph Neural Networks (GNNs) and Transformer models for property prediction, and Active Learning and Reinforcement Learning for decision-making.
• AI-Enabled Translational Science Insight: Experience in applying AI/ML methods to support translational science, such as identifying response biomarkers or for patient stratification by analyzing clinical or real-world data (RWD).
• Thought Leadership: A strong record of impactful publications in top-tier AI/ML conferences (e.g., NeurIPS, ICML, ICLR) or leading drug discovery/computational science journals, and patents on novel AIDD methods or molecular designs are considered a significant advantage.