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FlixBus is seeking an AI Transformation Lead to drive AI-first transformation across the Commercial Intelligence organization. This is a high-impact individual contributor role that bridges technical depth and organizational influence, working closely with Data Science, Engineering, Marketing, and senior leadership teams.
Key Responsibilities:
- Own end-to-end delivery of AI initiatives across Commercial Intelligence: scoping, prioritization, production deployment, and adoption, with clear success criteria and on-time delivery
- Evangelize an AI-first culture within CI and across the organization through workshops, best-practice sharing, and internal communities of practice
- Identify, pilot, and scale AI use cases across CI and the broader organization; evaluate emerging AI tools and platforms; partner with engineers and data scientists to move proof-of-concepts into production
- Manage cross-functional stakeholders (technical and non-technical), align diverse teams, and remove blockers to accelerate AI adoption
- Translate complex AI concepts into clear, compelling narratives for senior leadership, marketing, and finance
- Ensure quality and rigor of attribution and measurement frameworks, acting as connective tissue between technical teams building AI capabilities and the broader organization adopting them
The role offers a hybrid work model (office-first with flexibility), up to 60 days per year working from another location, travel perks (12 free Flix vouchers + 12 discount vouchers), wellbeing support, learning & development opportunities, and a mentoring program.
Requirements:
- Master's or PhD in Data Science, Computer Science, Economics, Statistics, Engineering, or related quantitative field (or equivalent practical experience)
- 5+ years of experience in data science, AI/ML, or analytical roles with demonstrated track record of leading complex, cross-functional AI projects from conception to delivery
- Proven ability to manage multiple workstreams simultaneously, align diverse stakeholders, and deliver results in fast-moving environments
- Strong working knowledge of modern AI and ML methods, tooling, and best practices; hands-on experience with Python and the broader data/AI ecosystem (cloud platforms, MLOps, LLMs, experimentation frameworks)
- Genuine passion for AI and its potential to reshape how organizations work; constant experimentation and knowledge-sharing mindset
- Nice-to-haves: experience with causal inference or econometric methods, background in digital marketing or growth analytics, familiarity with vendor evaluation and AI governance frameworks
