Associate Director, AI Solutions Scientist

The Associate Director, AI Solutions Scientist is a hands‑on experienced technical leader who specializes in architecting and developing modern AI solutions, use‑cases, and applications as well as efficiently conducting research on feasibility of such solutions. This role focuses on business utility and how AI transforms processes, providing architectural and design guidance to cross‑functional teams and sometimes implementing state‑of‑the‑art AI solutions for business processes within R&D and across corporate functions.

The AI Solutions Scientist will develop leading‑edge technical solutions such as multi‑agent orchestration, pragmatic uses of generative AI, and robust reusable AI solution capabilities as part of cross‑functional teams including life sciences subject matter experts, technical development teams, AI engineers, and AI platform engineers. In a rapidly evolving AI world, the AI Solutions Scientist remains current with emerging technologies, guiding better implementation of projects and offering cross‑functional guidance when needed.

Collaboration is central: the Scientist works with Data Science, AI Scientists, and engineers within the larger data science and AI team to design, develop, and implement robust solutions for Otsuka. Partnerships with IT, AI platform engineers, and other stakeholders ensure the delivery of efficient, effective solutions that advance AI transformation.

• AI product strategy: develop a product vision and roadmap specifically for AI‑driven solutions, aligning AI capabilities with business objectives, technology, and market trends.
• Data‑driven decision making: use data analysis and KPIs to monitor product performance and make informed decisions, considering the unique evaluation metrics for AI models in pharma R&D and enterprise use cases.
• Understanding of pharma R&D data: possess a deep understanding of data in drug development, clinical trials, external healthcare data, and responsibilities for responsible AI, privacy by design, and regulatory compliance.
• User‑centric solution design and development: deliver effective AI‑enabled products that build trust, drive adoption, and enable transformation while embracing user needs and responsible AI principles.
• AI and ML models: experiment with, develop, and train or fine‑tune high‑quality AI models for business problems and processes, validating and evaluating them for fielding within holistic solutions.
• Generative AI: expertise in generative AI, including prompt engineering, embeddings, and fine‑tuning, and quantitative and qualitative evaluation of large language models.
• Agentic AI frameworks and architecture: design, implement, and deploy agentic AI systems utilizing perception, planning, reasoning, orchestration, execution, and reflection loops.
• Understanding MLOps and LLMOps: possess strong knowledge of processes and tools for deploying and maintaining ML/LLM models and agents in production.
• Guide AI ecosystem capabilities: provide technical input on AI platform, frameworks, and architecture, guiding developers and the wider team.
• Use case review: lead or assist in reviewing AI/ML use cases to ensure alignment with AI guidelines, responsible AI, and platform capabilities.
• Development of reusable AI components: promote reusable data and AI solution components across business functions, accelerating innovation.

• Cross‑functional team leadership: collaborate with technical, semi‑technical, and business stakeholders to lead diverse teams, including data scientists, engineers, designers, marketing, legal, and executives.
• Stakeholder management: guide stakeholders in communicating AI progress, outcomes, impact, limitations, and risks.
• "Translator" communication: bridge communication between technical AI teams and non‑technical business stakeholders.
• Enablement and change management: lead adoption of new AI technologies within the organization.
• Partnerships: partner with internal and external teams to co‑develop AI/ML capabilities, ensuring alignment with enterprise architecture and compliance.

• Risk management and compliance: collaborate with legal,…