Data Scientist

Grade Level (for internal use): 12 S&P is a leader in risk management solutions leveraging automation and AI/ML. This role is a unique opportunity for hands-on ML scientists and NLP/Gen AI/ LLM scientists to grow into the next step in their career journey and apply her or his technical expertise in ML, deep learning, GenAI, and LLMs to drive business value for multiple stakeholders while conducting cutting-edge applied research around LLMs, Gen AI, and related areas..     Responsibilities and Impact:   ML, Gen AI, NLP, LLM Model Development: Design and develop custom ML, Gen AI, NLP, LLM Models for batch and stream processing-based AI ML pipelines. Model components will include data ingestion, preprocessing, search and retrieval, Retrieval Augmented Generation (RAG), NLP/LLM model development, fine-tuning and prompt engineering and ensure the solution meets all technical and business requirements. Work closely with other members of data science, MLOps, technology teams in the design, development, and implementation of the ML model solutions.   Internal Collaboration: Collaborate closely with product teams, business stakeholders, MLOps, machine learning engineers, and software engineers to ensure smooth integration of machine learning models into production systems.   Documentation: Write and Maintain comprehensive documentation of ML modeling processes and procedures for reference and knowledge sharing.   Develop Models Based on Standards and Best Practices: Ensure that the models are designed and developed while adhering to specified standards, governance and best practices in ML model development as specified by senior Data Science and MLOps leads.   Assist in Problem Solving: Troubleshoot complex issues related to machine learning model development and data pipelines and develop innovative solutions.     What We’re Looking For:   Basic Required Qualifications:   Master’s or Ph.D degree in Computer Science, Mathematics or Statistics, Computational linguistics, Engineering, or a related field.   5+ years of professional hands-on experience leveraging large sets of structured and unstructured data to develop data-driven tactical and strategic analytics and insights using ML, RAG, and NLP.   Capable of implementing machine learning techniques to tackle business challenges.   Knowledgeable in statistical analysis and data visualization tools (such as Pandas, Matplotlib, and Seaborn) for analysing and presenting data insights.   Solid understanding of statistics and machine learning techniques.   Proficient in feature engineering methods to improve model performance.   Experienced in A/B testing and experimental design to assess the effectiveness of models.   Should be able to produce high-quality Python code suitable for production.   Capable of integrating large language models (LLMs) or multimodal models into production systems.   Familiar with various generative AI frameworks, including LangGraph and LangChain.   Skilled in prompt engineering techniques to optimize the performance of generative AI models.   Knowledgeable in fine-tuning LLMs and constructing retrieval-augmented generation (RAG) pipelines.   Working knowledge of Agentic AI and MCP concepts.   Familiar with the PyTorch and TensorFlow frameworks.   Experienced with the AWS cloud platform.   Proficient in Git-based workflows, including GitHub Actions, CI/CD, and version control.   Knowledgeable about MLOps tools such as Docker and Kubernetes. About S&P Global Market Intelligence At S&P Global Market Intelligence, a division of S&P Global we understand the importance of accurate, deep and insightful information. Our team of experts delivers unrivaled insights and leading data and technology solutions, partnering with customers to expand their perspective, operate with confidence, and make decisions with conviction. For more information, visit www.spglobal.com/marketintelligence . What’s In It For You? Our Mission: