Manager, Data Science, Barcelona

Manager, Data Science

We are seeking an experienced Data Science / Machine Learning Engineering Lead to join our team and drive the development of advanced ML / AI capabilities. You will lead a team of Data Scientists / ML Engineers, focusing on building and deploying cutting-edge machine learning solutions using our modern ML infrastructure including Anthropic, OpenAI, and self-hosted LLMs.

• Team Leadership Management
• Lead, mentor, and develop a team Data Scientists, Data Engineers, ML Engineers
• Conduct regular 1 : 1s, performance reviews, and career development planning
• Foster a collaborative, innovative team culture focused on continuous learning
• Coordinate work allocation and ensure timely delivery of projects
• Facilitate knowledge sharing and best practices across the team
• Technical Leadership
• Design and implement scalable ML model training pipelines using modern toolset (e.g MLflow, Comet, Langfuse, WandB, Trino, dbt, Spark, Flink, etc)
• Lead fine-tuning initiatives for both commercial (Anthropic Claude, OpenAI GPT) and open-source LLMs
• Utilise self-hosted LLM infrastructure using Ray, AIBrix, and vLLM for optimal performance and cost efficiency with Lora / QLora
• Architect and oversee model continous validation frameworks within our ecosystem
• Develop real-time anomaly detection systems leveraging for streaming data processing
• Build predictive models for system performance, usage patterns, and automation workflow optimization
• Establish ML engineering best practices for model versioning, monitoring, and deployment on Kubernetes
• Creation of eval, validation and metrics pipelines for models during training and inference
• Strategic Initiatives
• Optimize the balance between commercial APIs (Anthropic, OpenAI) and self-hosted models for different use cases
• Partner with product and engineering teams to identify high-impact ML opportunities
• Define the teams technical roadmap aligned with company objectives
• Drive adoption of state-of-the-art ML techniques and tools
• Contribute to infrastructure decisions for scaling our ML platform
• Operational Excellence
• Implement robust CI / CD pipelines for ML models in Kubernetes environments
• Monitor model performance using MLflow tracking and implement drift detection
• Manage Flink jobs for real-time feature engineering and anomaly detection
• Document processes, architectures, and decision rationale

Requirements

Qualifications / Experience / Technical Skills

Soft Skills / Personal Characteristics

Python, PyTorch, TensorFlow