Research Senior Scientist, Cheminformatics AI/ML

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Job Description

Objective / Purpose:

We are seeking a skilled cheminformatic scientist to join Takeda’s cheminformatics team. This role will be key to fostering a “prediction-first” culture, integrating cheminformatics, computational chemistry, and machine learning to accelerate drug design. The candidate will collaborate closely with global teams in medicinal chemistry, DMPK, and AI/ML, defining a roadmap for computational tools that empower medicinal chemists to drive innovation.

Accountabilities:

• Develop and apply agentic AI solutions for drug discovery, including autonomous or human-in-the-loop workflows that integrate scientific reasoning, molecular design, data analysis, and computational tools.
• Identify opportunities throughout the DMTA cycle where predictive tools can drive acceleration. Build predictive models for molecule design and optimization. Extract medchem design knowledge from large-scale chemical and biological datasets.
• Establish and maintain MLOps capabilities for predictive models, including reproducible training and deployment pipelines, model lifecycle orchestration, performance monitoring, versioning, and scheduled retraining.
• Build, maintain, and enable efficient search and analytics across ultra-large chemical databases, including scalable data ingestion, standardization, indexing, similarity and substructure search, and distributed computing workflows.
• Stay updated with emerging trends in computational chemistry and machine learning as applied to drug discovery. Implement and execute innovative computational methodologies and tools such as AI-based drug discovery, generative chemistry, synthesizable chemical space exploration, DNA Encoded Library (DEL) data analysis and DEL/ML application.
• Serve as a trusted thought partner, helping to drive ideation and execution of innovative chemistry strategies that meet Takeda’s therapeutic goals.

Education & Competencies (Technical and Behavioral):

• PhD in Computational Chemistry, Cheminformatics, Computer Science or related disciplines, with 2+ years of experience in pharmaceutical or biotech setting.
• Demonstrated experience developing and applying agentic AI systems in drug discovery, including integrating and orchestrating chemical and biological databases, foundation models, scientific software tools, and multi-step computational workflows.
• Hands-on experience with MLOps and model lifecycle management, including workflow orchestration, CI/CD, model registries, monitoring, version control, and deployment in cloud or high-performance computing environments.
• Experience building, maintaining, and searching ultra-large chemical databases using scalable data engineering, distributed computing, and high-performance search technologies; familiarity with cloud storage, vector search, and chemical indexing is highly desirable.
• Solid programming and scripting capabilities (e.g., Python, R, C/C++) with a proven ability to design and automate scalable computational workflows.
• Strong expertise in cheminformatics toolkits]]>