Director of Data Science & AI

Glovo is part of the Delivery Hero Group, the world’s pioneering local delivery platform, our mission is to deliver an amazing experience—fast, easy, and to your door. We operate in around 65 countries worldwide. Delivery Hero has been listed on the Frankfurt Stock Exchange since 2017 and is part of the MDAX stock market index.

We are seeking a Director of Data Science and AI, Recommender Systems and Martech to lead ourmachine learning initiatives focused on user discovery and top-of-funnel growth. In this pivotal role, you will lead the teams responsible for the AI engines that power our recommendation systems and the marketing technology (Martech) stack specifically designed for user acquisition.

You will jointly architect state-of-the-art recommendation models that help new and existing users discover the best content, while simultaneously driving the AI strategy for high-efficiency growth marketing. Your leadership will be instrumental in developing cutting-edge methodologies—such as MMM, bidding optimization, and LTV-based acquisition models—to ensure we are acquiring the right customers at the right price across 65+ global markets.

Operating at a massive scale, you will bridge the gap between product discovery and performance marketing. Your ability to leverage deep learning and causal inference at org level will be critical in optimizing our global marketing spend and ensuring that every new user's first experience on the platform is personalized and high-converting.

Key Responsibilities

• Advanced Personalization & Discovery: Proven ability to guide and evaluate architectures in designing architectures for discovery, including deep learning models such as Two-Tower models, and Transformers for session-based behavior, to ensure users find relevant vendors instantly upon entering the platform.

  
  
  

• Acquisition-Focused Martech: Deep understanding of applying statistics and causal inference to optimize the top of the marketing funnel. This includes building models for Media Mix Modeling (MMM), automated bidding for performance channels (SEM, Paid Social), and predictive Long-Term Value (LTV) to guide acquisition strategy.

  
  
  

• Causal Inference & Incremental Growth: Deep understanding of establishing frameworks to measure the true incrementality of marketing spend and recommendation features.

  
  
  

• Cross-Functional Growth Leadership: Demonstrated ability to collaborate and align effectively across Growth Marketing, Performance Marketing, and Product Engineering teams to drive shared goals around efficient scaling and market expansion.

  
  
  

• Scalable MLOps for Real-Time Bidding & Ranking: Proven leadership in developing high-throughput data and feature pipelines. Ensure the efficient training and deployment of models that can handle massive external signals from marketing platforms and internal real-time user data.

  
  
  

• Technical Vision and Strategic Roadmap: Maintain knowledge at the forefront of AI/ML trends in both Recommendations and Growth Tech. Actively drive the team to adopt cutting-edge methodologies in generative content for ads and automated data quality monitoring.

  
  
  

• Agentic Modeling & DS Excellence: Lead the strategic adoption of agentic AI frameworks across your org. This involves designing autonomous and semi-autonomous AI agents capable of reasoning and tool-use to automate complex acquisition workflows—such as autonomous campaign optimization and self-healing data pipelines—while establishing rigorous guardrails for safety, reliability, and ethical governance.

  
  
  

• Extensive Leadership Experience: Demonstrated track record of building and scaling high-performing data science and AI teams from the ground up, with proven success deploying production-grade models in domains such as Recommendation Systems, E-commerce, or Marketing Science.

  
  
  

• Advanced Academic Background: A master’s degree or higher in a quantitative field such as Computer Science, Statistics, Mathematics, Physics, or a related discipline.

  
  
  

• Deep Technical Proficiency: Strong technical foundation as a previous individual contributor; ability to engage technically with the team's stack in Python and SQL, and critically evaluate work across deep learning frameworks (PyTorch, TensorFlow, or JAX) and data orchestration at scale (PySpark, Airflow).

  
  
  

• Cloud-Scale Personalization Expertise: Demonstrated expertise in managing and deploying large-scale AI solutions within cloud environments (GCP/AWS), specifically handling high-volume behavioral data and real-time event streams.

  
  
  
• Strategic Communication: Exceptional ability to translate complex AI concepts and algorithmic trade-offs into clear business implications for senior leadership and non-technical stakeholders.