Senior Data Scientist AI/ML

Awin is a company where data is central to operations, focusing on leveraging data across the group to support commercial decision-making and campaign management. The role is within the Core Platform team, responsible for developing, governing, and enabling Awin's AI platform to build scalable and responsible AI solutions. Awin is part of the Axel Springer group.

• Bachelor's degree or higher in Statistics, Mathematics, Data Science, AI/ML, or related field.
• 6+ years of experience in Data Science or AI, with exposure to platform-level enablement or ML Ops environments.
• Expertise in generative AI models and tooling, including standardized evaluation, AI-specific observability, safe rollback mechanisms, and retrieval-quality assessment.
• Experience designing scalable AI architecture components, model pipelines, inference patterns, vector search, feature engineering standards, and monitoring frameworks.
• Proven experience creating and maintaining end-to-end AI tools.
• Proven ability to mentor teams on AI methodologies, experimentation, and platform tooling adoption.
• Collaborative, proactive, and customer-focused approach to enabling product and engineering teams.
• Strong collaboration skills with Data Engineers, ML Engineers, and Software Engineers on platform-level solutions.
• Deep understanding of AI/ML lifecycle needs, feature stores, experiment tracking, model evaluation, governance, and reproducibility.
• Expertise in modern AI and generative AI frameworks, including LLM orchestration, retrieval-augmented generation (RAG), agentic workflows, and prompt engineering best practices.
• Ability to assess AI use-cases, guide teams on model selection and design patterns, and ensure responsible, secure AI usage.
• Ability to translate business and product requirements into platform-level capabilities, reusable components, and best-practice guidelines.
• Focus on cost stewardship across AI systems considering efficiency, resource usage, and value-for-impact.

• Define, maintain and drive adoption of AI/ML standards, patterns, and reusable components for efficient model development.
• Partner with Data Engineering and Platform Engineering to evolve AI platform capabilities ensuring scalability, governance, and robustness.
• Advise product teams on model design, data readiness, evaluation strategies, and responsible AI practices.
• Develop and implement frameworks, guidelines, and reference architectures for enterprise-wide AI and generative AI adoption.
• Evaluate new AI technologies, tools, and workflows to improve the platform continuously.
• Develop and maintain an AI experimentation sandbox for prototyping and testing models and tooling.
• Ensure data quality, feature standardization, and reproducibility across the AI lifecycle.
• Mentor and coach teams on AI methods, experimentation, and best-practice platform usage.
• Communicate complex concepts clearly to stakeholders to enable strategic decision-making and alignment.