Associate Director, AI Solutions Scientist

About the role

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.

Responsibilities

• Develop a product vision and roadmap specifically for AI-driven solutions, aligning AI capabilities with business objectives, technology, and market trends.
• 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.
• 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.
• 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.
• 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.
• Design, implement and deploy of agentic AI systems utilizing perception, planning, reasoning, orchestration, execution, and reflection loops.
• Provide technical input on AI ecosystem, AI platform, AI frameworks and architecture including AI solution evolution, and new capability development.
• 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.
• 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.
• 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.
• Guide and manage stakeholders in communicating AI progress, outcomes, impact, limitations, and risks to stakeholders and managing expectations.

Requirements

• 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.
• 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.
• 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.
• 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.

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.
• 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.
• 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.
• 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.

Pay

Minimum $169,222.00 - Maximum $253,000.00, plus incentive opportunity: The range shown represents a typical pay range or starting pay for individuals who are hired in the role to perform in the United States. Other elements may be used to determine actual pay such as the candidate’s job experience, specific skills, and comparison to internal incumbents currently in role. Typically, actual pay will be positioned within the established range, rather than at its minimum or maximum. This information is provided to applicants in accordance with states and local laws.