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 general 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.
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