Okay, I've searched for Senior Data Scientist roles specializing in NLP, LLM, and Generative AI in New York, NY, requiring 1+ years of experience and full-time availability. I have found 47 positions for you.

Sure. Here's the analysis:

Job Analysis:

The Senior Data Scientist role specializing in NLP, LLM, and Generative AI at S&P Global Ratings centers on leveraging cutting-edge AI technologies to deliver actionable insights and advanced language-based solutions that directly impact business stakeholders. The core purpose is to design, develop, evaluate, and deploy sophisticated machine learning and NLP models, including those based on large language models and generative AI, within production environments that handle both batch and real-time data. This requires not only strong technical expertise in deep learning frameworks such as TensorFlow and PyTorch, familiarity with Transformer architectures, and skills in prompt engineering and fine-tuning, but also the ability to translate business requirements into reliable AI systems. Collaboration is key—working cross-functionally with data science peers, MLOps, software engineers, and product teams ensures that models align with organizational standards and business needs. A successful candidate will confront challenges such as maintaining model performance and robustness in complex, data-rich environments, navigating ambiguity in emerging AI technologies, and integrating novel AI advances into established workflows. Excellence in this role means driving innovation while ensuring the scalability, reliability, and governance of AI models that uphold S&P Global’s mission of delivering trusted and transparent insights. The role demands a balance of technical rigor, strategic problem-solving, and clear communication, making it as much about applied research and pragmatic deployment as about coding and model building.

Company Analysis:

S&P Global is a venerable leader in financial market intelligence and risk assessment, with a reputation for providing authoritative credit ratings, analytics, and workflow solutions across multiple global markets. Positioned as a stable yet innovative engine within the world of finance, S&P Global is embracing AI and data science to sharpen its insight delivery and operational impact, reflecting its pursuit of continuous progress in a complex economic landscape. The company’s culture emphasizes integrity, discovery, and partnership, underscoring a work environment that values careful stewardship of information alongside innovation and collaborative problem-solving. For the data scientist role, this translates to a setting where high ethical standards in model governance are expected, alongside encouragement to experiment and lead in emergent AI fields like GenAI and LLM development. The large, global organization structure likely means this role has visibility and influence that spans multiple teams and geographies, requiring agility in communication and coordination. Strategically, hiring a senior NLP/GenAI specialist aligns with S&P Global’s mission to transform data into Essential Intelligence®, suggesting that this role is both a growth hire designed to infuse new technological capabilities and a strategic investment to maintain leadership through AI-driven insights. Candidates should expect a forward-thinking but disciplined corporate environment, where success involves blending technical innovation with adherence to industry standards and regulatory compliance.

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

• Behavioral: Tell me about a time when you had to develop an NLP or machine learning model to solve a complex business problem. How did you approach understanding both the technical and business requirements?
• Behavioral: Describe a situation where you had to collaborate with cross-functional teams, such as MLOps and software engineering, to deploy a machine learning model. What challenges did you face and how did you overcome them?
• Behavioral: Give an example of a time when you identified performance issues or biases in an AI model. What steps did you take to address them?
• Behavioral: Share an experience where you worked under ambiguity while implementing a new AI technology. How did you manage uncertainty and ensure progress?
• Technical: How do you approach designing and fine-tuning a large language model for business-specific applications?
• Technical: Explain your experience with Retrieval Augmented Generation (RAG) and how you would implement it in an NLP pipeline for scalable, real-time usage.
• Technical: What methods and metrics do you rely on to evaluate and validate NLP or GenAI model performance, especially in production scenarios?
• Technical: Walk me through how you would implement an end-to-end machine learning pipeline, including data ingestion, preprocessing, model development, evaluation, and deployment.
• Situational: Imagine a key NLP model you developed is performing well offline but shows degraded results in production. How would you diagnose and resolve the issue?
• Situational: You receive conflicting feedback from a product team and engineering team on the requirements for an LLM-based solution. How would you approach reconciling these differences?
• Situational: The field of Generative AI evolves rapidly and a newly published model architecture promises better performance. How do you decide whether to integrate this new approach into your projects?
• Company Fit: What interests you most about working at S&P Global Ratings and contributing in the NLP/GenAI space?
• Company Fit: How do you see your personal values aligning with S&P Global’s stated emphasis on integrity, discovery, and partnership?
• Company Fit: Considering S&P Global’s position in financial markets and risk analytics, how would you ensure your AI models are both innovative and compliant with industry regulations?
• Questions for Interviewer: Can you describe the typical team structure and collaboration style for data science projects here at S&P Global?
• Questions for Interviewer: How does S&P Global support continuous learning and exploration of emerging AI techniques for its data scientists?
• Questions for Interviewer: What are the immediate business challenges or projects that the new Senior Data Scientist in NLP and GenAI will be expected to contribute to?