The Senior Data Scientist will be responsible for developing, optimizing, and applying AI-based solutions to address complex business challenges, combining technical expertise with strategic skills and leadership. **Responsibilities** **Development and optimization** of LLMs, including model architecture, RAG, fine-tuning, and performance analysis. **End-to-end project management** of LLM-based solutions, from conception through production implementation. **Interdisciplinary collaboration** with engineering, product, and design teams to integrate LLMs into products and services. **Bias analysis and mitigation** in models, promoting ethical AI use. **Research and innovation** in NLP (Natural Language Processing) techniques and emerging trends in the field. Technical leadership and mentoring of junior data scientists. **Academic Qualifications** Bachelor’s degree in fields such as Computer Science, Computer Engineering, Mathematics, Statistics, or related disciplines. Graduate degree (Master’s or PhD) in Machine Learning, NLP, Data Science, or related areas. **Hard Skills** Proficiency in frameworks such as TensorFlow, PyTorch, and Hugging Face. Advanced knowledge of **Transformer architectures** (e.g., GPT, BERT, T5). Practical experience deploying LLMs in production environments. Strong ability in data manipulation and analysis using **Python, R, SQL**, and libraries such as Pandas and NumPy. Familiarity with MLOps (e.g., CI/CD for machine learning models). Experience optimizing models for performance and cost (quantization, pruning, etc.). **Soft Skills** Excellent communication skills to translate technical concepts into terms understandable by non-technical stakeholders. Critical thinking and ability to solve complex problems. Capacity to lead cross-functional teams and manage priorities. Curiosity and continuous learning to explore new technologies and approaches. **Preferred Qualifications** Publications or participation in AI/NLP conferences (e.g., NeurIPS, ACL). Contributions to open-source LLM projects. Experience with ethics and responsibility in AI application. Experience handling large-scale data and Big Data (e.g., Spark, Hadoop). Certification in Machine Learning/Deep Learning (e.g., TensorFlow Developer Certificate, AWS Certified Machine Learning). Cloud certifications (e.g., Azure AI Engineer, Google Cloud Professional ML Engineer). **Benefits** Collaborative environment focused on innovation. Opportunity to work on strategic projects with direct organizational impact. Incentives for training and continuous development. ;