Associate Director, AI Solutions Scientist

* **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 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* 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.* 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.* Experience in software/product engineering. 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, AWS SageMaker or other data science platforms* 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)* Highly self-motivated to deliver both independently and with strong team collaboration* 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.* 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.* Strong internal and cross-functional collaboration, project management skills with a focus on delivering impactful initiatives.* Understanding of life sciences R&D business processes.* Experience working with relevant life sciences datasets such as claims, clinical trial data, regulatory data, quality data, and other life sciences operations datasets.* An understanding of data's role in AI, including data collection, governance, and how to structure a problem for better AI outcomes.* 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 within R&D, is highly desirable.* Proven track record of implementing proof of concept as well as production grade AI/ML, Gen AI and large language model (LLM) applications in production environments.**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.This job description is intended to describe the general nature and level of the work being performed by the people assigned to this position. It is not intended to include every job duty and responsibility specific to the position. Otsuka reserves the right to amend and change responsibilities to meet business and organizational needs as necessary.If you are a #J-18808-Ljbffr