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

Sure. Here's the analysis:

Job Analysis:

The Senior Data Scientist role at S&P Global Ratings is fundamentally designed to harness advanced NLP, Large Language Models (LLMs), and Generative AI to create impactful, scalable machine learning solutions that drive business value and improve risk management capabilities. This role is highly technical, demanding hands-on involvement in designing, developing, fine-tuning, and deploying AI models, particularly in cutting-edge domains like transformer architectures and Retrieval Augmented Generation (RAG). The candidate must not only possess strong programming skills, especially in Python and frameworks such as Hugging Face, PyTorch, TensorFlow, but also be adept at handling large-scale data ingestion, preprocessing, and model evaluation pipelines. Cross-functional collaboration is key here; the role bridges data science, ML Ops, software engineering, and business stakeholders, requiring the ability to translate complex algorithms into production-ready, reliable, and scalable systems. Critical success factors include delivering technically robust NLP/LLM models aligned with business needs, demonstrating innovation through applied research, and ensuring high model performance and efficiency in real-world environments. The role’s implied challenges include navigating ambiguity in emerging AI technologies, adapting models to varied and evolving data sources, and maintaining governance and standards in ML lifecycle. Success within the first year would likely be measured by the candidate’s ability to independently deploy impactful NLP/GenAI solutions, enhance model evaluation frameworks, and contribute strategically to projects that advance S&P Global's AI capabilities in ratings and analytics.

Company Analysis:

S&P Global occupies a pivotal position as a trusted market leader providing essential intelligence, including credit ratings and data-driven analytics, to global capital and commodity markets. Its stature as a stable yet innovation-driven company means that this data scientist will operate in an environment that values both precision and cutting-edge technology. The company culture emphasizes integrity, discovery, and partnership—values that encourage curiosity, rigorous research, and close collaboration internally and with clients. Given S&P Global’s focus on sustainability, risk management, and market transparency, the role is aligned with the company’s strategic priority to integrate advanced AI to enhance product offerings and deliver clear, actionable insights to clients worldwide. The position likely reports into a senior data science or AI leadership team, granting visibility and influence across multiple product lines, while also requiring the agility to work within well-established operational standards and regulatory frameworks. Growth potential is significant as the company seeks to embed AI deeply into its core services, making this a strategic hire not just for stabilizing existing models but for pioneering innovation in NLP and GenAI applications that shape how financial markets digest and utilize information.

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

• Behavioral: Tell me about a time when you had to collaborate across multiple teams (e.g., data science, engineering, product) to deploy a machine learning model. How did you ensure smooth integration and stakeholder alignment?
• Behavioral: Describe a challenging problem you encountered while fine-tuning an NLP or LLM model. What approach did you take to solve it and what was the outcome?
• Behavioral: Give an example of a situation where you had to troubleshoot a pipeline or model performance issue in production. How did you identify the root cause and implement a solution?
• Behavioral: How have you balanced the need for innovation and adherence to governance or industry standards in previous ML projects?
• Technical: Walk me through your process of designing a Retrieval Augmented Generation (RAG) system. What components are essential and how do you evaluate its effectiveness?
• Technical: How would you implement and optimize a transformer-based NLP model to handle both batch and real-time data processing pipelines?
• Technical: Explain your experience with fine-tuning large language models. What strategies do you use to ensure model generalizability and robustness?
• Technical: What tools and practices do you use for model evaluation in NLP projects to monitor accuracy, bias, and consistency across evolving datasets?
• Situational: Imagine you deploy a GenAI-powered feature that begins generating inconsistent or erroneous outputs after a software update. How would you approach diagnosing and resolving this?
• Situational: If a business stakeholder requests a rapid development of an NLP model for a new use case outside your expertise, how would you manage expectations and deliver results?
• Situational: You're assigned to lead a proof-of-concept project involving open-source LLM platforms and integrating them into S&P’s credit risk workflows. What steps would you prioritize to ensure success?
• Company Fit/Motivation: Why are you interested in working at S&P Global, particularly within the Ratings division focusing on NLP and GenAI?
• Company Fit/Motivation: How do your personal and professional values align with S&P Global’s stated values of integrity, discovery, and partnership?
• Company Fit/Motivation: What attracts you to a company that is both a market leader and committed to advancing sustainability and risk transparency through technology?
• Questions for Interviewer: How does the data science team at S&P integrate with other departments like product and engineering during the model deployment lifecycle?
• Questions for Interviewer: What are the current biggest challenges the team faces in scaling NLP and GenAI solutions across S&P Global Ratings products?
• Questions for Interviewer: How does S&P Global support continuous learning and innovation for technical talent working on emerging AI technologies like LLMs?