Information Technology & Software
8 Jun
Lead Data Scientist
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.
