Lead AI Researcher

Responsibilities:

• Domain Expertise: Serve as a domain expert on Generative and Agentic AI for Credit Solutions. Attain fluency in AI applications, including the use of open-source and internal tools, to enhance AI expertise within Credit Solutions.

• Hands-On Application: Utilize internal and external AI applications offered by S&P Global to support credit risk analysis and decision-making.

• Technology Monitoring: Closely monitor emerging technologies, industry adoption, and associated risks, including strengths, limitations, and regulatory guidelines concerning Gen-AI in credit risk.

• Use Case Development: Develop and test new credit risk use cases utilizing data and AI applications within S&P Global across multiple platforms, including data wrangling and cross-referencing.

• Data Science and Statistics:

  • Analyze quantitative and qualitative data from various content sets linked to credit ratings within S&P Global to understand their contributions to credit risk surveillance and risk management workflows.

  • Collaborate with the Credit Solutions Thought Leadership team to develop and work on internal applications within a sandbox environment.

• Thought Leadership:

  • Author thought leadership content independently and represent S&P Global at webinars and conferences, focusing on AI advancements in credit risk management.

This role offers invaluable exposure to client workflows and is pivotal in driving Credit Solutions' digital transformation journey. You will have the opportunity to evolve into a thought leader in AI and credit risk management while gaining hands-on experience with our internal and external AI tools.

What We're Looking for:

• Master’s or Ph.D. degree in Data Science, Statistics, Quantitative Finance, Artificial Intelligence, or a related quantitative/computational field.

• 5-10 years of relevant experience in AI/ML, quantitative roles, or data science. Ph.D. graduates in a statistics/data science/quant finance discipline from an internationally accredited university with hands-on experience in Gen-AI projects may be considered with 0-5 years of experience.

• Proficient in Python, R, and SQL

• Experience in accessing data via feeds, cloud, or REST API preferred.

• Familiarity with GenAI frameworks (e.g., RAG, Agentic AI) and libraries including Hugging Face, TensorFlow, PyTorch, or Scikit-learn preferred.

• Prior experience in developing predictive analytics, data wrangling of structured and unstructured data, Natural Language Processing, or other emerging AI and automation use cases in financial services.

• Some knowledge of testing and validating AI models using techniques such as cross-validation, A/B testing, and performance metrics.

• Strong communication skills with a proven ability to solve problems and engage effectively across functions.

  • Self-starter with a proactive mindset, capable of working independently across multiple groups and stakeholders.