Associate Director AI Activation

Job Description

The Associate Director, AI Activation & Data Science is a member of the AI Activation team within Digital Human Health (DHH), supporting the Specialty, Pharma & Infectious Diseases business units. This role is designed for a highly technical AI leader with deep, hands-on expertise in building and deploying AI solutions, including generative AI, large language models (LLMs), and agentic systems, who is eager to apply that expertise to pharmaceutical commercial challenges. Working in close partnership with brand, marketing, Market Engagement (ME), and commercial analytics colleagues, this leader ensures AI solutions are grounded in real business needs. The Associate Director leads the technical design and delivery of strategic AI programs, defines scalable AI architectures and guardrails, and moves AI use cases from prototype to production with measurable business value, while upholding responsible AI, compliance, and governance standards.

Primary Job Function and Key Responsibilities:

AI solution architecture and delivery:

• Own the technical design and end-to-end delivery of DHH-wide strategic AI initiatives from ideation and prototyping through deployment and adoption.
• Design and build generative AI and agentic solutions, including LLM applications, retrieval-augmented generation (RAG), multi-agent workflows, tool and API integrations, and simulation or scenario-planning capabilities.
• Define scalable AI architectures, reusable components, and delivery guardrails so that AI use cases can be operationalized and democratized across ME teams.
• Establish rigorous evaluation approaches for AI solutions, covering accuracy, groundedness, reliability, latency, cost, and safety, and monitor performance in production.
• Lead MLOps/LLMOps practices, including versioning, CI/CD, monitoring, and model lifecycle management, in partnership with IT and platform teams.
• Stay current on the rapidly evolving AI landscape (models, frameworks, and tools) and assess which internal and external capabilities should be adopted.

AI enablement partnership and governance:

• Run targeted AI pilots in prioritized areas of business impact (e.g., field and customer engagement tools, targeting support, forecast validation, barrier analysis, causal inference), rapidly prototyping and validating value with business partners.
• Partner closely with brand, ME, and commercial analytics teams to understand business needs and translate them into technical requirements and solution designs.
• Activate and guide external delivery partners and vendors, setting technical standards and reviewing solution quality.
• Apply a structured prioritization approach to triage AI requests, and guide teams toward approved enterprise AI tools and platforms.
• Champion responsible AI practices, ensuring solutions meet data privacy, security, model validation, and enterprise governance standards.
• Mentor team members and build technical AI capability across the business, establishing best practices for prompt engineering, agent design, and AI development.

Required Experience & Skills:

• 5+ years of experience in AI/ML engineering, applied data science, or a related technical field, including at least 2 years of hands-on experience building generative AI or LLM-based solutions. Prior pharmaceutical or healthcare industry experience is required.
• Deep expertise in LLMs and generative AI, including RAG, embeddings and vector databases, agent frameworks (e.g., LangChain, LangGraph, Semantic Kernel), prompt engineering, and fine-tuning.
• Proven track record of taking AI solutions from prototype to production at enterprise scale, including solution architecture, APIs, cloud services for building AI and agents (e.g., Google Cloud Vertex AI, Amazon Bedrock), and MLOps/LLMOps.
• Experience designing evaluation frameworks and guardrails for AI systems, grounded in responsible AI principles and governance.
• Advanced Python and SQL skills, experience with Databricks or similar platforms, and strong software engineering practices (e.g., Git, testing, CI/CD), backed by a solid foundation in machine learning and statistics.
• Explains complex AI concepts in plain business language to non-technical stakeholders and senior leaders, with a product mindset focused on user needs, adoption, and measurable business value.
• Aligns brand, ME, IT, and cross-functional stakeholders on priorities, and manages scope by redirecting out-of-scope requests while maintaining strong relationships.
• People and/or matrix leadership experience, including leading cross-functional teams, global collaborators, and external delivery partners and vendors.
• Strong organizational and project management skills; resilient and adaptable in ambiguity and a fast-moving AI landscape.

Qualifications:

• Master's degree or PhD in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related technical discipline is required.

Required Skills:

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