Associate Director

Associate Director, Ai And Data ScientistThe associate director, AI and data scientist is a hands-on experienced technical leader who specializes in applying AI and data science in the context of pharma R&D operational processes towards AI transformation. This may involve leading or contributing to AI projects that leverage diverse data and information to augment and accelerate current workflows. The role will focus on developing data science, 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 leading the implementing state-of-the-art AI solutions for business processes within R&D and across corporate functions. The AI and data scientist will develop leading-edge technical solutions such as multi-agent orchestration, pragmatic uses of generative AI, and robust reusable AI and data science 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 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 and data 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.AI product strategy: develop a product vision and roadmap specifically for AI-driven solutions, aligning AI capabilities with business objectives, technology, and market trends. Implement data science and AI portfolio objectives and contribute to the development of data and analyses strategies esp. in augmenting and accelerating R&D operations while leveraging 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. Investigate and conduct Proof of Concept (PoC) initiatives and develop solutions for new AI applications using advanced technologies like Large Language Models (LLMs) and Generative AI (Gen-AI) to enhance data analytics capabilities to advance and effectively accelerate candidates across drug development phases. 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. 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 as well as direct vendors to provide oversight on AI concepts and their implementation. Remain current with industry trends and advancements in AI/ML, R&D processes and data, providing insights to help team leadership in influencing the organization's technical roadmap and strategy. Identify and apply innovative analytical solutions with a strong focus on adopting novel AI tools, methodologies, and technologies, including Gen AI, AI, machine learning applied to internal and external data 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 Enablement and change management: lead efforts to support the adoption of new AI technologies within an organization. Develop processes to optimize data and analytics systems and their execution, ensuring responsible use of cutting-edge technological advancements. 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 production support of data analytics and AI applications Development and promote reuseable AI components: ensure development of reusable data and AI solution components and promote their use across the data and AI ecosystem, business functions (e.g., clinical operations, asset management, quality, safety, regulatory, RWD, Enterprise functions, etc.) and promote innovative, scalable data and AI approaches to accelerate data science and AI solutions Cross-functional team leadership: collaborate with a mix of technical, semi-technical and business stakeholders to lead and align diverse teams, including data scientists, engineers, designers, marketing, legal, and executives. Stakeholder management: guide and manage stakeholders in communicating AI progress, outcomes, impact, limitations, and risks to stakeholders and managing expectations. "Translator" communication: the skill to bridge communication between technical AI teams and non-technical business stakeholders. Partnerships: partner with other functional areas internally and external partners to conceptualize, develop or co-develop AI/ML capabilities while leveraging AI Engineering, Data Engineering, and AI platform architecture, AI platform engineers, and infrastructure, and other IT teams. Collaborate with internal data and AI scientist, IT, cloud architects to ensure that data infrastructure and technical solutions are aligned with enterprise architecture and compliance needs. Must leverage capabilities and roles that exist in the team and other areas Risk management and compliance: collaborate with legal, privacy, and ethics teams to address concerns around algorithmic bias, fairness, transparency, and data privacy. Adaptability: ensure effective operations while deeply understanding the greater ambiguity inherent in AI product development and adapting to continuous experimentation and iteration cycles. Strategic thinking: ability to think beyond features and focus on curating intelligence and context that drives product evolution.Qualifications masters degree in data science, computer engineering, computer science, physics, statistics, information systems, or a related discipline with focus on advanced and modern data science, including the use of AI and machine learning. PhD is preferred. Expertise in real-world data assets and using them to generate scientific evidence and guide operational effectiveness and efficiencies. Deep expertise across data engineering, representation, Gen AI, AI and machine learning techniques and experience in architecting and delivering AI/ML use cases. Experience in AI product development with focus on leveraging AI, Data Science, Machine Learning. Deep understanding of AI and Machine Learning and its applications in Pharma Experience with data science and AI enabling technology, such as Dataiku Data Science Studio, Snowflake, AWS SageMaker or other data science platforms and ability to maintain awareness as new AI technologies emerge Creative problem solving using responsible use of AI and other technologies. Excellent communication and stakeholder management skills, with the ability to convey complex technical concepts to non-technical audiences. Familiarity with machine learning and AI technologies and their integration with data engineering pipelines Strong understanding of Software Development Life Cycle (SDLC) and data science development lifecycle (CRISP). Awareness of testing and validation approaches related to GxP, non-GxP, etc. Highly self-motivated to deliver both independently and with strong team collaboration, leverage roles that exist in the team and in the larger ecosystem Experience in AI and ML based software/product engineering; familiarity with test and validation principles, GxP validation Experience with data science enabling technology, such as Dataiku Data Science Studio, Snowflake, AWS SageMaker or other data science platforms Experience in architecting, building and maintaining large-scale data and AI solutions in a scientific, regulated, or research-heavy environment. Strong experience working within the pharmaceutical, biotech, or life sciences industry, particularly in drug development and clinical trials is highly desirable. Proven track record of implementing and deploying Gen AI and large language model (LLM) applications in production environments. An understanding of data's role in AI, including data collection, governance, and how to structure a problem for better AI outcomes. Competencies accountability for results - stay focused on key strategic objectives, be accountable for high standards of performance, and take an active role in leading change. Strategic Thinking & Problem Solving - Make decisions considering the long-term impact to customers, patients, employees, and the business. Patient & Customer Centricity - Maintain an ongoing focus on the needs of our customers and/or key stakeholders. Impactful Communication - Communicate with logic, clarity, and respect. Influence at all levels to achieve the best results for Otsuka. Respectful Collaboration - Seek and value others' perspectives and strive for diverse partnerships to enhance work toward common goals. Empowered Development - Play an active role in professional development as a business imperative.