About the Role

We are looking for a hands-on Lead Data Scientist to shape and drive AI/ML strategy across multiple product lines. This is a high-impact role combining deep expertise in classical machine learning with practical experience in generative AI, ensuring solutions are cutting-edge, production-ready, and scalable.

You will lead a small, talented team, guiding end-to-end ML development while fostering technical excellence and innovation.

Key Responsibilities

Team Leadership & Strategy

• Lead a small, multidisciplinary AI/ML team.

• Align AI/ML initiatives with product goals while mentoring and developing team members.

Model Development

• Design, implement, and optimize ML models for tasks including classification, regression, clustering, and forecasting.

• Build pipelines for training, evaluation, and testing to ensure model robustness, accuracy, and reproducibility.

Inference & Deployment

• Collaborate with engineers to operationalize models for production.

• Ensure efficient inference and seamless integration into live systems.

Generative AI & Advanced Applications

• Explore and implement solutions using LLMs, vector databases, retrieval-augmented generation (RAG), and agent frameworks (e.g., LangChain, LangGraph).

• Translate cutting-edge AI research into practical, impactful applications.

Collaboration & Innovation

• Work closely with AI Engineers, Data Scientists, and product teams to deliver scalable, production-ready AI/ML features.

• Stay up-to-date on both classical ML and generative AI trends to maintain a competitive edge.

Mentorship

• Provide guidance, code reviews, and knowledge sharing to support the growth of junior team members.

Requirements

• Experience: 5+ years as a Data Scientist or ML Engineer with hands-on coding and model development.

• Leadership: At least 1 year mentoring or leading a small team.

• Classical ML Expertise: Strong experience with scikit-learn, XGBoost, LightGBM, and other regression/classification/clustering algorithms.

• ML Lifecycle Knowledge: Training, testing, inference, continuous evaluation.

• Generative AI: Practical experience with LLMs and GenAI frameworks (e.g., LangChain, HuggingFace, CrewAI).

• Programming: Proficient in Python with clean, maintainable, efficient coding practices.

• Experimentation & Statistics: Solid foundation in experimental design and statistical methods for robust, reproducible models.

• Cloud & MLOps: Familiarity with AWS (preferred) and MLOps practices.

• Communication: Excellent problem-solving and cross-functional collaboration skills.

• Language: Fluent English for effective communication in a distributed global team.

Why Join

• Hybrid Work Model: Enjoy a flexible combination of office and remote work in Madrid.

• Learning & Development: Grow in an open, creative environment with opportunities to learn from experts.

• Collaborative Team Culture: Join a strong, multidisciplinary team where ownership and decision-making are shared.

• Early-Stage Impact & Career Growth: Contribute to a fast-growing AI startup with international reach.

• Competitive Compensation: Attractive economic package aligned with experience.