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Data Science Team Lead
hace 2 meses
Robert Walters Spain is seeking a highly skilled and experienced Data Science Team Lead to join their dynamic team in Barcelona, Spain.
Key Responsibilities- Lead the development of innovative machine learning solutions, including Natural Language Processing (NLP), to drive business growth and innovation.
- Collaborate with the AI Engineering Team to integrate ML models into our systems, covering pre-processing, fine-tuning, and deployment.
- Drive efforts to optimize ML model performance through in-depth data analysis and validation in real-world applications.
- Develop data enrichment pipelines aligned with strategic goals, delivering tangible value.
- Stay updated on advancements in ML, NLP, and IR technology, incorporating emerging trends for continuous improvement.
- Translate business requirements into technical solutions, working with stakeholders across the company.
- Lead a team of data scientists to construct and maintain robust AI/ML pipelines, coordinating with vendors for cutting-edge technologies.
- Provide mentorship and guidance to foster collaboration and professional development within the AI Engineering Team.
- Experience with leadership and hands-on technical expertise within the field of machine AI, ML, or data science, with at least 3 years of leadership experience.
- Experience with LLMs APIs, including pre-processing and deployment on cloud-based infrastructure.
- Proven experience with ML and NLP frameworks and libraries (e.g., PyTorch, HuggingFace, LangChain, spaCy, NLTK, scikit-learn, etc.).
- Proficiency in Git for collaborative project development and version control.
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related STEM field.
- Proficiency in Python and/or other high-level programming languages commonly used in machine learning.
- Demonstrated expertise in information retrieval, utilizing NLU techniques for structured data extraction.
- Proficiency in cloud-based infrastructure and managing large datasets.
- Familiarity with software engineering best practices, ensuring the development of secure, scalable, and efficient production systems.
