Associate Director, AI Solutions Scientist
Otsuka Pharmaceutical Co., Princeton, NJ, United States
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 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
Collaboration 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. Enablement and change management:
Lead efforts to support the adoption of new AI technologies within an organization. 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
Strategic thinking and Responsible AI 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: A bility to think beyond features and focus on curating intelligence and context that drives product evolution.
Qualifications/ Required Knowledge/ Experience and Skills: 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.
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
Collaboration 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. Enablement and change management:
Lead efforts to support the adoption of new AI technologies within an organization. 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
Strategic thinking and Responsible AI 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: A bility to think beyond features and focus on curating intelligence and context that drives product evolution.
Qualifications/ Required Knowledge/ Experience and Skills: 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.