Associate Director of Data Science – RAG, NLP, LLM and GenAI (Hybrid or Remote) - S&P Global : Job Details

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Grade Level (for internal use): 12

The Role: Associate Director of Data Science – RAG, NLP, LLM and GenAI

S&P is a leader in risk management solutions leveraging automation and AI/ML. This role offers an opportunity for hands-on ML scientists and NLP/Gen AI/LLM scientists to grow in their careers, applying their technical expertise to drive business value and conduct cutting-edge research in LLMs, GenAI, and related areas.

• ML, Gen AI, NLP, LLM Model Development: Design and develop custom models for AI/ML pipelines, including data ingestion, preprocessing, search, retrieval, RAG, NLP/LLM development, fine-tuning, and prompt engineering, ensuring solutions meet technical and business requirements. Collaborate with data science, MLOps, and technology teams.
• Model Evaluation: Develop and maintain evaluation tools to assess model performance, accuracy, and reliability. Optimize models for efficiency.
• Model Deployment: Work with MLOps team to deploy models into production, ensuring scalability and reliability.
• Internal Collaboration: Partner with product teams, stakeholders, MLOps, ML engineers, and software engineers for seamless integration.
• Documentation: Maintain comprehensive documentation of modeling processes and procedures.
• Standards and Best Practices: Ensure models adhere to organizational standards, governance, and best practices.
• Problem Solving: Troubleshoot complex issues and develop innovative solutions.

• Bachelor's, Master's, or Ph.D. in Computer Science, Mathematics, Statistics, Computational Linguistics, Engineering, or related fields.
• 8+ years of experience with structured and unstructured data analytics using ML, RAG, and NLP.
• 3+ years experience with Python, Hugging Face, TensorFlow, Keras, PyTorch, or similar tools.
• 3+ years developing NLP models, especially with transformer architectures.
• Experience with large-scale search and retrieval solutions, semantic search, embeddings.
• Hands-on experience with LLMs and GenerAI development and tuning.
• Familiarity with open-source platforms, research projects, Kaggle competitions is a plus.