Summary: The AI Lead defines and executes Alira Health's AI strategy, focusing on practical integration and measurable efficiency gains across regulated life sciences work. Highlights: 1. Lead AI strategy and adoption roadmap for a global team 2. Drive AI integration into day-to-day business processes 3. Steer the AI Center of Excellence operating model Are you being referred to one of our roles by a connection in Alira Health? If so, please apply using the referral link emailed to you. Join our global team dedicated to innovation and initiative, where physical walls and different time zones don’t limit, but encourage, collaboration. Where all contributions and new ideas are explored with an open mind and work is driven by our shared values: be courageous, be accountable, be honest, be inclusive and elevate others. Job Description Summary Job Description THE AI LEAD ROLE The AI Lead is accountable for defining and executing Alira Health’s AI strategy and adoption roadmap across IEC and EVG, ensuring alignment with business priorities, data privacy requirements, and governance standards appropriate for regulated life sciences work. This role leads the practical integration of AI into day\-to\-day business processes in 2026, with a specific focus on scalable operational enablement and measurable efficiency gains across functions in scope. The AI Lead owns the organization’s phased AI approach for 2026: (Phase I) Resource identification and onboarding; (Phase II) Requirements definition and use\-case intake; (Phase III) AI roadmap and long\-term development strategy—partnering closely with IS\&T, PMO, and Operations \& Business Leaders to execute each phase with clear decision rights and risk controls. The role also steers the AI Center of Excellence (AI CoE) operating model, including standardized training and guidance materials, monitoring of adoption and outcomes, and strengthening IT/processes to enable scalable value while minimizing shadow IT risk. KEY RESPONSIBILITIES AI Strategy, Portfolio \& 2026 Delivery Model (35%)* Enterprise AI Roadmap: Define and maintain the enterprise AI roadmap and long\-term development strategy based on validated requirements and feasibility assessments. * Phased Execution Ownership: Drive the 2026 phased approach end\-to\-end (Phase I–III), ensuring accountable delivery and smooth onboarding of the AI function and operating rhythm. * Portfolio Prioritization: Establish transparent intake and prioritization criteria by business value and strategic relevance, in partnership with PMO and Operational units, and maintain a consolidated portfolio view. AI Governance, Risk Management \& Compliance (35%)* Centralized Governance Model: Implement a centralized AI governance model aligned with business strategy, data security, and compliance expectations (including documentation, approvals, and lifecycle controls). * Shadow IT Minimization: Strengthen IT/process guardrails to enable scalable AI value while reducing shadow IT risks, in coordination with IS\&T and leadership stakeholders. * Data Governance Foundation: Define the corpus of organizational knowledge essential for operations and set access boundaries based on confidentiality and compliance constraints; support centralization via the enterprise data warehouse and back\-office knowledge maintenance processes. * Feasibility \& Readiness Controls: Ensure feasibility checks are performed for proposed AI initiatives (data availability, technical constraints, AI readiness), partnering with IS\&T and PMO support to prevent over\-commitment and unmanaged risk. Operational Enablement, Adoption \& Performance Measurement (20%)* Standardized Adoption Framework: Establish SOPs, formal training programs, and continuously updated guidance materials to support consistent and responsible AI adoption across teams. * Continuous Performance Assessment: Implement ongoing measurement of usability, efficiency gains, and output quality using user surveys and operational data (e.g., timesheet data) alongside practice\-level economic performance indicators. * AI Committee Reporting: Provide decision\-ready reporting to the AI Committee and senior leadership on adoption, delivery progress, risks, and benefits realization against defined metrics. AI\-Enabled Reporting \& KPI Automation Focus (10%)* AI\-Enabled Reporting Team (Design \& Oversight): Lead the design and implementation oversight of an AI\-enabled reporting capability that provides accurate, reliable tools and up\-to\-date visibility on company performance through clear reporting, aligned with evolving business needs. * Operational Reporting Automation: Partner with stakeholders to automate reporting processes for 2026 scope, improving efficiency and quality while reducing operational risk through improved data quality and governance controls. Decision\-Making Authority* AI Governance Standards (Delegated Authority): Define and enforce required governance controls (documentation, access boundaries, approval pathways) for AI solutions and tools, escalating exceptions and material risks to the COO/AI Committee as appropriate. * Portfolio Recommendations: Recommend prioritization, sequencing, and resourcing decisions for AI requirements/use cases, aligned with PMO prioritization and feasibility checks performed with IS\&T support. * Adoption \& KPI Targets: Own adoption measurement approach and performance metrics definition (e.g., implementation completion %, user satisfaction/acceptance, long\-term planning maturity, feasibility assessment). DESIRED QUALIFICATIONS \& EXPERIENCE* Master’s degree (preferred) in Computer Science, Data Science, Biostatistics, Engineering, or related field (PhD a plus). * 8–12\+ years of experience across data science/ML, analytics, or AI delivery, including 3–5\+ years leading teams and/or enterprise programs. * Demonstrated experience implementing AI governance and scalable adoption in regulated or quality\-driven environments (healthcare, pharma, medtech, CRO, or adjacent). * Proven cross\-functional leadership with IS\&T/IT, PMO, Operations and business stakeholders in a matrix organization. * Location: Barcelona, Spain (Hybrid with strong preference for in\-office presence for collaboration) TECHNICAL COMPETENCES \& SOFT SKILLS Technical competencies:* Strong applied ML/analytics understanding plus practical implementation oversight (validation mindset, monitoring expectations, documentation discipline). * Familiarity with MLOps concepts and secure\-by\-design delivery practices; ability to translate governance needs into workable technical standards. * Strong grasp of data governance and privacy\-by\-design concepts, including confidentiality boundaries and access controls. * Advanced business English proficiency (written and verbal). Additional languages are a plus. Soft Skills:* Executive presence; strong influence and alignment\-building across practices and functions. * High judgment and risk awareness; balances innovation with governance and compliance. * Structured, pragmatic operator; drives measurable outcomes and adoption. * Strong coaching and enablement orientation. Languages English Education Masters of Science (MS): Computer and Information Science, Masters of Science (MS): Data Processing, Masters of Science (MS): Engineering Contract Type Regular