Director of Data Science & AI

apartmentDelivery Hero placeBarcelona calendar_month 

Experteer Overview

As Director of Data Science and AI, Recommender Systems and Martech, you will lead global ML initiatives powering discovery and top-of-funnel growth. You’ll architect state-of-the-art recommendation models and drive the Martech stack for user acquisition across 65+ markets.

You shape strategy around MMM, bidding optimization, and LTV-based acquisition to optimize spend and conversion. You operate at scale with cross-functional partners to deliver personalized first experiences and high-impact growth.

Compensaciones / Beneficios
  • Guide architecture design for advanced discovery (Two-Tower models, Transformers) to improve instant relevance for new and existing users
  • Develop and optimize acquisition-focused Martech, including MMM, automated bidding for SEM and Paid Social, and predictive LTV
  • Establish causal inference frameworks to measure incremental impact of marketing spend and recommendations
  • Lead cross-functional collaboration with Growth Marketing, Performance Marketing, and Product Engineering
  • Build scalable MLOps pipelines for real-time bidding and ranking with high-throughput data and external signals
  • Set the technical vision and roadmap for AI/ML trends in Recommendations and Growth Tech; promote generative content for ads and data quality monitoring
  • Drive adoption of agentic AI frameworks to enable autonomous optimization and self-healing data pipelines with governance
Responsabilidades
  • Proven leadership in building and scaling data science and AI teams with production-grade models in recommendations, e-commerce, or marketing science
  • Master's degree or higher in a quantitative field
  • Strong Python and SQL skills; experience with DL frameworks (PyTorch, TensorFlow, JAX)
  • Experience with cloud AI solutions in GCP/AWS and handling large-scale real-time event streams
  • Ability to communicate complex AI concepts to senior leadership and non-technical stakeholders
  • Hands-on experience with data orchestration at scale (PySpark, Airflow)
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