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. While keenly focusing on business utility, and how AI transforms processes, the role will go deep into providing architectural and design guidance on the use of AI and data science, new processes and workflows to  cross-functional teams and at times 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 and technical development teams, including AI engineers and AI platform engineers. In a world with rapidly evolving AI, AI technology, the AI Solutions scientist will also keep current with evolving technologies, gaining experience in new technology to guide better implementation of projects in responsibility, but also providing guidance to cross-functional teams, where needed.


The AI Solutions Scientist will work with other Data Science, AI Scientists, AI engineers, and others within the larger Data Science and AI team in designing, developing, and implementing robust solutions for Otsuka. The role will partner with cross-functional teams such as IT (e.g., AI platform engineers to utilize recommended foundational capabilities, and architectural frameworks) and other stakeholders to ensure that efficient and effective solutions are developed and ultimately lead to AI transformation.

Technical and data skills

• 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 key performance indicators (KPIs) to monitor product performance and make informed decisions, considering the unique evaluation metrics for AI models in delivering business value, esp. in Pharma R&D operations and Enterprise use cases.
• Understanding of Pharma R&D Data: Possess a deep and expansive understanding of data in the field of drug development, clinical trials, external healthcare data to be able to be effectively build AI solutions that conform to responsible AI, privacy by design, as well as regulatory compliance.
• User centric solution design and development: Deliver effective AI enabled products that build trust, drive adoption, and lead to transformation. Ensure a design centric approaches through a deep understanding of user needs, fears, processes, regulations, and responsible AI. 
• AI and ML Models: Experiment with, develop and train or fine-tune high quality effective AI models for business problems and processes, validate and evaluate them for fielding as part of broader solutions. Demonstrate strong foundational understanding of AI/ML, statistics, and data science concepts.
• Generative AI: Expertise in generative AI, including concepts like prompt engineering, embeddings, and fine-tuning, is often required for building and upgrading modern AI solutions. Core understanding of evaluation of LLMs quantitatively and qualitatively. Hands-on experience demonstrated in developing and fielding enterprise fieldable AI systems.
• Agentic AI frameworks and architecture: Design, implement and deploy of agentic AI systems utilizing perception, planning, reasoning, orchestration, execution, and reflection loops. Demonstrate deep previous experience in architecting and deploying AI agent-based solutions.
• Understanding MLOps and LLMOps: Possess strong knowledge of processes and tools for deploying and maintaining machine learning models, LLM’s, and agents in a production environment. Oversee the life cycle management and revisions of AI solutions
• Guide AI ecosystem capabilities: Provide technical input on AI ecosystem, AI platform, AI frameworks and architecture including AI solution evolution, and new capability development. Guide developers and other technical team members and provide oversight on AI concepts and their implementation
• Use case review: Lead or assist in review of AI / ML use cases to ensure a AI guidelines, frameworks, platform components, and responsible AI is enabled. Act as a subject matter expert for AI solution on cross functional teams in bespoke organizational initiatives by providing thought leadership and execution support for data engineering needs. Demonstrate a proactive approach to identifying and resolving potential issues both during development and p