Head of AI Programming

Title:

Company:

About Ipsen:

Ipsen is a mid-sized global biopharmaceutical company with a focus on transformative medicines in three therapeutic areas: Oncology, Rare Disease and Neuroscience. Supported by nearly 100 years of development experience, with global hubs in the U.S., France and the U.K, we tackle areas of high unmet medical need through research and innovation.

Our passionate teams in more than 40 countries are focused on what matters and endeavor every day to bring medicines to patients in 88 countries. We build a workplace that champions human-centric leadership and fosters a culture of collaboration, excellence and impact. At Ipsen, every individual is empowered to be their true selves, grow and thrive alongside the company’s success. Join us on our journey towards sustainable growth, creating real impact on patients and society!

For more information, visit us at https://www.ipsen.com/ and follow our latest news on LinkedIn and Instagram.

Job Description:

WHAT - Summary & Purpose of the Position

The Head of AI-Programming evolves Ipsen’s Biometry capability into an AI-enabled and automation-embedded function, building the standards, tools, applications and ways of working needed to deliver Machine Learning (ML) and Large Language Models (LLM) driven analyses, output automation, application development and information science across R&D.

The role leads clinical data scientists and AI-programming experts across statistical programming, clinical data, data engineering, ML, LLM-enabled analytics and application development, partnering across R&D, IT, Digital/Data/Analytics/AI and external providers to turn high-value use cases into governed, reusable solutions.

The role accelerates analysis and reporting through automation, AI-programming, applications and information science capabilities, ensuring solutions are robust, documented, validated where required, reproducible and scalable.

As a peer Biometry capability, the role works across Biometry to enable an AI-native model that improves quality, speed, productivity, decision-making.

WHAT - Main Responsibilities & Technical Competencies

Main Responsibilities & Technical Competencies

• Lead the AI-Programming capability within Biometry, setting the strategy, roadmap, priorities, standards and operating model for AI-enabled programming, automation, application development and information science.
• Build and lead a specialist team of clinical data scientists, AI-programming experts and application developers, including workforce planning, capability development, coaching, prioritization, delivery oversight and succession planning.
• Lead the development and adoption of automation capabilities that improve the speed, quality, consistency and scalability of Biometry deliverables and ways of working.
• Build and scale ML, LLM and AI-enabled analytical capabilities that support priority clinical, statistical, programming and R&D decision-making use cases.
• Oversee the design and delivery of fit-for-purpose applications, tools and data products that make Biometry expertise, data and insights easier to access, apply and reuse.
• Create governance for AI-enabled programming, covering use case intake, risk assessment, validation strategy, documentation, version control, access control, model monitoring, lifecycle management and user training.
• Partner with Statistical Programming, Biostatistics, Statistical Innovation, Clinical Development, Data Management, IT, Digital/Data/Analytics/AI and external providers to turn high-value use cases into governed, scalable solutions.
• Evaluate external tools, vendors and platforms, making clear buy-versus-build recommendations based on business value, technical feasibility, compliance, security, scalability and long-term maintainability.
• Drive adoption across Programming, Biostatistics and asset teams through training, communities of practice, reusable playbooks, demonstrations of value and hands-on support for priority studies.

HOW - Behavioural Competencies Required

Competency Competency Behavioural Markers Explanation of Choice

Ensures Accountability

• Ensures single accountable referents per task/project/outcome (independent of organizational context or multi-team projects)
• Builds and anchors an environment where people have the skills and habits to ask for clarification when accountabilities are unclear
• Consults/seeks relevant stakeholder views/expertise and coaches/ensures decisions are made by consent vs. consensus
• Takes personal accountability for decisions, actions, successes and failures, and fosters the same for others
• Follows through on commitment and makes sure others do the same

Owns the AI-programming roadmap, solution quality, compliance, prioritization, delivery and adoption of reusable automation and clinical data science capabilities.

Develops/Coaches Talent

• Able to identify and align career goals, and blend organizational objectives into a cohesive development plan for self and team
• Able to coach
• Provides structured, actionable, regular and directional feedback and acts as a coach to empower people to own their own growth/development
• Prepares their own succession plans
• Displays a radically human-centered mindset; puts people first; focuses on doing good
• Demonstrates ability to build team effectiveness

Builds a small specialist team of clinical data scientists and AI-programming experts while upskilling Programming and Biometry colleagues in modern tools and workflows.

Drive Vision and Strategy

• Paints a compelling picture of the vision (future status quo) and strategy that motivates others to action Sets the vision for Programming to evolve from manual execution to an AI-enabled, automation-first, information-science capability that improves speed, quality and productivity.

Manage Complexity

• Identifies contradictory information/demands/inputs to effectively solve problems
• Develops and evaluates alternative scenario and solutions
• Able to identify what truly matters and ruthlessly focus/ prioritize on making decisions with real impact Balances technical complexity, GxP expectations, data quality, security, validation, user adoption, vendor choices, and practical delivery across a global matrix.

Communicates Effectively

• Asks open questions and digs deeper; shows care and respect for different perspectives (both verbally and non-verbally) and able to relate to other points of view
• Communicates transparently, "tells it how it is" while keeping the communication respectful
• Builds clear and crisp messages with structure and focus, uses visual communication and storytelling to make the message easy to digest and connect with the outcomes
• Demonstrates gravitas

Translates complex AI, ML, LLM, application development and data engineering concepts into clear, actionable messages for study teams, senior leaders, IT and external partners.

HOW - Knowledge & Experience

Knowledge & Experience (essential):

• 10+ years of relevant pharmaceutical, biotechnology, technology, data science or statistical programming including significant leadership experience.
• Strong understanding of clinical trials, statistical programming and clinical data science, including clinical data flow, analysis datasets, outputs, validation.
• Demonstrated experience developing or scaling automation, application development, data products, analytical tools or AI-enabled programming capabilities in a regulated environment.
• Practical knowledge of modern programming, data science and AI/ML approaches, including R, Python, SAS or equivalent environments, ML, LLMs, GenAI, responsible AI and data governance.
• Experience leading technical teams and delivery portfolios, including resourcing, prioritization, coaching, capability development, stakeholder management and change adoption.
• Ability to partner effectively with strong communication skills to translate technical trade-offs, risks and business value for senior stakeholders.
• Experience with clinical data standards, metadata management, validated computing environments, cloud platforms, version control or related technology platforms.

Knowledge & Experience (preferred):

• Experience in oncology, rare disease, neuroscience or specialty-care clinical development.
• Track record of delivering measurable productivity, quality or timeline improvements through automation, software engineering, data science or AI in a regulated setting.

Education / Certifications (essential):

• MS/MSc or PhD in statistics, computer science, data science, bioinformatics, mathematics, clinical informatics or another relevant quantitative or technical discipline.

Language(s) (essential):

• Fluent in English, with excellent verbal and written communication skills.

We are committed to creating a workplace where everyone feels heard, valued, and supported; where we embrace “The Real Us”. The value we place on different perspectives and experiences drives our commitment to inclusion and equal opportunities. When we include diverse ways of thinking, we make more thoughtful decisions and discover more innovative solutions. Together we strive to better understand the communities we serve. This means we also want to help you perform at your best when applying for a role with us. If you require any adjustments or support during the application process, please let the recruitment team know. This information will be handled with care and will not affect the outcome of your application.