Okay, I've searched for Senior Data Scientist positions focusing on NLP, LLM, and Generative AI in New York, NY. The roles are full-time and require at least a Bachelor's degree with 1+ years of experience. I have found 142 positions for you.

Sure. Here's the analysis:

Job Analysis:

The Sr Data Scientist role at S&P Global Ratings is designed for an experienced ML practitioner specializing in NLP, Large Language Models (LLMs), and Generative AI. The core purpose is to architect, develop, and operationalize advanced AI models that transform large volumes of structured and unstructured data into strategic, actionable insights supporting risk management and credit rating solutions. This role requires deep technical expertise not only in model development—such as fine-tuning transformers, implementing retrieval-augmented generation (RAG), and optimizing algorithms—but also in collaborating across cross-functional teams including MLOps, data science, and product stakeholders. The job demands balancing rigorous research innovation with pragmatic deployment and continual monitoring to maintain model performance and scalability in production. Candidates must display strong coding proficiency in Python and related ML frameworks like PyTorch and Hugging Face libraries, reflecting how data science at S&P is highly hands-on and tied closely to real-world impact. Success in this role will be measured by the ability to reliably deliver robust, explainable models that directly enhance business workflows and decision-making, while also helping to push the envelope on applied AI research within a well-governed, compliance-sensitive environment. The role implies navigating complexities from ambiguous data challenges, evolving regulatory standards, and evolving stakeholder needs, requiring adaptability, problem-solving, and clear communication skills.

Company Analysis:

S&P Global is a well-established leader in the financial information services industry, recognized globally for market intelligence, credit ratings, and analytics that empower organizations to make sound decisions amid complexity. As a division of the larger S&P Global entity, the Ratings team has a critical role in delivering transparency and clarity in the credit markets, leveraging technology to scale impact. The company’s culture appears to prioritize integrity, innovation, and collaboration, with a strong emphasis on driving progress through data and technology, while being mindful of regulatory compliance and ethical standards. For a senior NLP scientist, this environment means working at the intersection of cutting-edge AI and highly regulated financial services, requiring a balance of technical creativity with disciplined governance. The role fits into a cross-functional, multi-disciplinary ecosystem where partnership between data scientists, engineers, product managers, and business leaders is key. Given the company’s mission to provide essential intelligence with high accuracy and trustworthiness, this position is strategically aligned with advancing AI capabilities that ensure reliable automated insights. The company promotes learning, diversity, and long-term career growth, which suggests that candidates who demonstrate curiosity, agility, and shared values around discovery and partnership will thrive. Organizational transparency and openness to innovation provide fertile ground for pushing boundaries in GenAI, making this a prime opportunity for those eager to drive business value via transformative AI in a global, impactful setting.

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

• Behavioral: Tell me about a time when you had to troubleshoot a complex machine learning model that was underperforming. How did you identify and resolve the key issues?
• Behavioral: Describe an experience where you collaborated closely with cross-functional teams like DevOps or product management on deploying an AI solution. What challenges did you face and how did you overcome them?
• Behavioral: Give an example of when you had to quickly learn and implement a new NLP or machine learning technique. How did you ensure its successful application to your project?
• Behavioral: Share an instance where you worked on a project requiring you to balance innovation with compliance or governance policies. How did you manage the trade-offs?
• Technical: Walk me through how you would design and implement an NLP pipeline incorporating Retrieval Augmented Generation (RAG). What components and considerations are critical?
• Technical: What experience do you have with fine-tuning large language models? Describe your approach to prompt engineering and evaluation metrics used.
• Technical: How do you ensure the scalability and reliability of ML models when deploying them in a production environment? What are common pitfalls and best practices?
• Technical: Explain how you would approach semantic search at scale using embeddings. What trade-offs do you consider compared to keyword-based search?
• Situational: Imagine you discover that a deployed LLM is producing biased or unreliable results in certain scenarios. How would you address this issue both technically and from a stakeholder communication viewpoint?
• Situational: Suppose a critical data input to your model pipeline suddenly becomes unavailable. How would you respond to maintain business continuity while troubleshooting?
• Situational: If an internal product team requests a new feature that conflicts with existing compliance or governance rules, how would you handle this tension?
• Company Fit: Why do you want to work with S&P Global Ratings specifically, given your background in NLP and GenAI?
• Company Fit: How do you see the role of AI and large language models evolving in regulated industries like credit ratings, and how does that align with your career aspirations?
• Company Fit: S&P Global emphasizes integrity, discovery, and partnership as core values. Can you share how you embody these values in your professional work?
• Questions for Interviewer: What are the biggest technical challenges the NLP and GenAI teams at S&P Global are currently facing?
• Questions for Interviewer: How does S&P Global support continuous learning and development for data scientists working in emerging AI fields?
• Questions for Interviewer: Can you describe how collaboration between data science, engineering, and product teams is structured within the Ratings division?