Director, Applied AI Solutions, N.A. Commercial

The Director Applied AI Solutions – NA Commercial is responsible for defining and leading the North America Commercial AI strategy, portfolio, and roadmap to drive business performance, customer engagement, operational excellence, and innovation. This role identifies and prioritizes high-value opportunities across patient finding, HCP engagement, next-best-action, insights generation, and commercial operations, translating business needs into scalable, compliant AI-enabled capabilities that deliver measurable value.

Serving as the business owner and strategic leader for Commercial AI solutions, the incumbent partners across Commercial, Data Science, Digital/IT, Legal, Compliance, Privacy, and enterprise platform teams to drive governance, investment decisions, solution adoption, and value realization. The role provides thought leadership on emerging AI technologies, influences senior stakeholders, and ensures AI capabilities are aligned with commercial priorities, enterprise standards, and long-term growth objectives.

WHAT - Main Responsibilities

Build and deliver NA Commercial AI solutions (primary focus)

Design and build AI-powered workflows, agents, and applications for prioritized NA Commercial use cases, including patient finding, patient identification, HCP targeting, field insights, next-best-action, and content and insights automation.
Translate Commercial business problems into technical solution designs and executable delivery plans.
Build and test LLM, agentic AI, retrieval-augmented generation (RAG), and predictive AI solutions using approved Ipsen data and technology environments.
Convert analytics, models, and prototypes into scalable, compliant, and business-ready solutions that can be adopted by NA Commercial teams.
Develop evaluation frameworks for accuracy, grounding, hallucination risk, reliability, business KPIs, cost, and performance; iterate based on measured results.
Partner with the Data Science team to productionize models and prototypes and with IT/platform teams to deploy and support solutions.

Commercial use-case and product ownership

Partner with NA Commercial Operations, Brand, Field, Patient Services, Value & Access, and Analytics teams to identify and prioritize AI opportunities.
Maintain the roadmap and backlog for assigned NA Commercial AI solutions.
Define business requirements, user experience, adoption plans, and value measurement for solutions in scope.
Ensure solutions are integrated into Commercial workflows rather than delivered only as technical prototypes.
Gather business feedback and prioritize enhancements based on adoption and measurable impact.

AI solution and vendor evaluation

Conduct structured evaluations and pilots of AI tools relevant to NA Commercial use cases.
Define business, technical, compliance, cost, and scalability criteria; build test approaches and benchmark vendor claims.
Evaluate the suitability of approved platform AI capabilities, including Snowflake Cortex and Salesforce/Agentforce, for specific NA Commercial use cases.
Make evidence-based build-versus-buy recommendations in partnership with IT, Procurement, Legal, Compliance, Privacy, and relevant platform owners.
Ensure vendor solutions meet defined Commercial outcomes and align with approved architecture, security, privacy, and data-handling requirements.

Solution lifecycle, quality, and responsible AI

Define and manage the lifecycle for the NA Commercial AI solutions in scope, including deployment, versioning, evaluation, monitoring, and ongoing enhancement.
Establish fit-for-purpose testing for output quality, accuracy, grounding, hallucination risk, bias, reliability, cost, latency, and business performance.
Apply Ipsen privacy, compliance, security, and responsible AI requirements to NA Commercial solutions.
Partner with IT and platform teams on deployment controls, access management, monitoring, and production support.
Document solution logic, intended use, limitations, controls, and performance.
Contribute NA Commercial requirements, reusable components, and learnings to broader Ipsen AI standards and knowledge sharing.

HOW - Knowledge & Experience

Knowledge & Experience (essential)

Significant experience (10+ years) in software, machine learning, applied AI, or AI solution development, including a track record of delivering production solutions with measurable business impact.
Hands-on experience (typically 2+ years) building and deploying LLM/GenAI applications in production, including agentic workflows, RAG, grounding, and evaluation, delivered through frameworks or directly on foundation-model APIs.
Advanced hands-on proficiency in Python and SQL; experience building solutions using approved cloud AI/ML services.
Experience managing deployed AI solutions through monitoring, versioning, evaluation, quality controls, cost management, and CI/CD practices.
Demonstrated ability to translate ambiguous business needs into technical solutions and partner effectively with Commercial, Data Science, and technology stakeholders.
Experience owning an AI solution from use-case definition and development through adoption, performance measurement, and enhancement.
Advanced hands-on proficiency in Python and SQL; experience with APIs, integration patterns, containerization, and Git-based workflows.
Hands-on development of agentic AI and LLM applications, including orchestration, tool/function calling, RAG, prompt engineering, grounding, and evaluation.
Experience using frameworks such as LangGraph or LlamaIndex, or building directly on foundation-model APIs such as OpenAI, Anthropic, AWS Bedrock, or Azure OpenAI.
Experience building solutions with approved cloud AI/ML services; familiarity with vector databases and AI/LLM observability tooling such as LangSmith, Langfuse, MLflow, or equivalent.
Working knowledge of AI capabilities in NA data platforms, including Snowflake Cortex and Salesforce/Agentforce.
Practical experience with technical delivery controls, in