Okay, I've searched for Senior Data Scientist positions focusing on NLP, LLM, and Generative AI in New York and Washington, DC. These full-time roles require at least 1 year of experience and a Bachelor’s or Master’s degree in Computer Science or related fields. I have found 134 positions matching your criteria.

Sure. Here's the analysis:

Job Analysis:

The Senior Data Scientist role at S&P Global Ratings is fundamentally about leveraging advanced NLP, large language models (LLMs), and generative AI to enhance the company's risk management and analytics capabilities. This position is designed for a technically proficient practitioner who will directly contribute to designing, developing, fine-tuning, and deploying cutting-edge ML and NLP models that handle both batch and real-time data pipelines. Key responsibilities include model development—incorporating complex elements such as Retrieval Augmented Generation (RAG) and semantic search—and close collaboration with cross-functional teams including MLOps, software engineering, and product groups. This is not a purely research role; it requires pragmatic problem-solving skills to ensure models meet stringent business standards around accuracy, reliability, and scalability in production. The candidate must be comfortable navigating the full ML lifecycle, from data preprocessing through deployment and ongoing model optimization. The expectation is that the candidate will quickly identify practical solutions to emerging challenges in applying LLMs and GenAI to real-world financial datasets within a highly regulated and standards-driven environment. Success in this role means delivering robust, well-documented models that accelerate business insights while working seamlessly with diverse teams to integrate AI at scale. The role also implies a continuous learning mindset given the fast-evolving landscape of NLP and generative AI technologies.

Company Analysis:

S&P Global is a deeply established leader in financial analytics, credit ratings, and market intelligence, renowned for its rigorous data-driven insights that support confident decision-making across global capital markets. The firm is both stable and innovation-focused, blending tradition in high-integrity financial services with a forward-looking commitment to emerging technologies like AI and sustainability analytics. Their culture appears rooted in integrity, discovery, and partnership—values emphasizing ethical responsibility, curiosity-driven innovation, and teamwork with both internal and external stakeholders. This environment demands high professional standards, transparent collaboration, and adaptability in a complex regulatory landscape. The Senior Data Scientist role sits at a vital nexus of product, technology, and business teams, likely providing the individual with considerable visibility and impact across the organization, especially as AI and NLP solutions play a growing role in S&P Global’s competitive edge. Given S&P’s strategic push into sustainability and AI-enabled solutions, this hire supports key corporate priorities around digital transformation and market leadership. For the candidate, thriving here means aligning technical excellence with business acumen, embracing continuous innovation, and contributing to a mission-driven enterprise that balances legacy with agility.

Absolutely. Here are some mock interview questions that could come up:

• Behavioral: Tell me about a time when you had to develop or fine-tune an NLP model under tight business constraints. How did you prioritize requirements and ensure delivery?
• Behavioral: Describe a situation in which you collaborated with cross-functional teams like MLOps and product managers to deploy a machine learning model. How did you navigate differing priorities?
• Behavioral: Can you share an example of troubleshooting a complex machine learning pipeline or deployment issue? What was your approach, and what was the outcome?
• Behavioral: How have you kept current with rapid advances in NLP and LLM research, and how have you applied new techniques to your work?
• Technical: What are the key considerations and best practices when designing and fine-tuning large language models for production use?
• Technical: Explain your experience with Retrieval Augmented Generation (RAG). How would you implement and evaluate a RAG system to support semantic search in financial data?
• Technical: How do you ensure model evaluation metrics reflect real-world business outcomes, especially in NLP or generative AI projects?
• Technical: Describe your experience working with frameworks like Hugging Face, TensorFlow, or PyTorch to build and deploy transformer models.
• Situational: Imagine a deployed LLM model starts to show degraded performance after a recent data update. How would you investigate and resolve this issue?
• Situational: You receive conflicting feedback from business stakeholders about the utility of a newly developed AI-powered insight tool. How do you approach aligning technical capabilities with business needs?
• Situational: Suppose you need to design a new NLP pipeline that processes both batch and streaming datasets. What architectural choices and technologies would you consider?
• Company Fit/Motivation: Why are you interested in applying your NLP and AI skills within a financial services leader like S&P Global Ratings?
• Company Fit/Motivation: How do the values of integrity, discovery, and partnership resonate with your personal and professional approach to data science?
• Company Fit/Motivation: S&P Global emphasizes sustainable finance and energy transition analytics. How do you see AI and NLP contributing to these missions?
• Questions for Interviewer: Can you describe how the data science team collaborates with other functions within S&P Global Ratings to prioritize AI initiatives?
• Questions for Interviewer: What are the biggest technical and business challenges the team currently faces in deploying generative AI models at scale?
• Questions for Interviewer: How does S&P Global support continuous learning and professional growth for data scientists working with cutting-edge AI technologies?