We're looking for a Staff Data Scientist to lead the technical direction, scaling, and evolution of our core AI capabilities across two tightly connected domains: Search, Ranking & Recommendations: advancing our multi-stage retrieval and ranking systems (Two-Tower networks, Vector Search/ANN, LTR) to optimize catalog discovery for raw physical products and designs, with a particular focus on multimodal and sequence embeddings.
You'll drive systems that generate rich features and embeddings for recommendation engines, as well as tools that help merchants with content creation, smart product delayering, and automated visual asset processing.
You'll lead the modeling strategy and collaborate across our data science and machine learning engineering teams, advancing our production systems from early successes to enterprise-grade AI infrastructure.
Advance our personalization engines, incorporating sequence embeddings of merchant history, niche trends, and sales performance signals to surface the best products and designs.




Connect downstream merchant sales and conversion data back into upstream design generation and candidate ranking models to continuously improve recommendation relevancy.
Deepen our classical computer vision capabilities (OpenCV) alongside deep learning vision models (e.g., ResNet, CNNs, YOLO/segmentation networks) for delayering, automated background separation, print-area quality analysis, and asset validation.
Driving end-to-end product innovation (maker focus)Act as a product engineer with full technical autonomy across the stack - from foundational model selection (fine-tuning, prompt engineering, self-hosting) to serving pipelines and frontend workflows.
Technical leadership, governance & mentorshipDefine and refine online/offline evaluation frameworks (NDCG, MRR, visual quality metrics, merchant conversion uplift) and oversee our continuous A/B testing strategy.
Set standards for applied ML across the organization, mentor senior ICs, run design re

📌 Staff Data Scientist (Madrid)
🏢 AlleyCorp
📍 Madrid