Okay, I've searched for Senior Data Scientist roles specializing in NLP, LLM, and Generative AI in Chicago, IL, requiring 1+ years of experience, full-time positions, and a Bachelor's degree in Computer Science or related fields. I have found 47 positions for you.

Sure. Here's the analysis:

Job Analysis:

The Sr Data Scientist role specializing in NLP, Large Language Models (LLMs), and Generative AI at S&P Global Ratings is fundamentally designed to develop and operationalize advanced AI/ML models that extract actionable business value from complex, often unstructured data sources. The role balances hands-on model design, fine-tuning, and deployment with collaboration among data scientists, MLOps engineers, and product teams, reflecting a need for both technical depth and cross-functional agility. The emphasis on large-scale search and retrieval, Retrieval Augmented Generation (RAG), and prompt engineering highlights the advanced nature of the challenges, requiring innovative approaches to integrating LLM-based solutions into real-world financial risk and credit analytics. The candidate must not only be skilled in Python and deep learning frameworks like TensorFlow or PyTorch but also adept at navigating the specific demands of NLP models tailored for financial data contexts. Success here means building robust, scalable, and interpretable AI models that meet stringent performance criteria, integrate seamlessly into production pipelines, and ultimately empower stakeholders with deeper, more accurate insights for decision-making. Furthermore, adherence to best practices and governance signals both regulatory awareness and a commitment to maintainable, transparent AI solutions. The role thus demands strategic thinking, a curiosity for cutting-edge AI research, and the practical problem-solving skills needed to troubleshoot and optimize complex pipelines under real-world constraints.

Company Analysis:

S&P Global Ratings operates at the intersection of finance, technology, and data analytics, providing critical intelligence that influences global capital markets. As a trusted market leader within S&P Global’s broader ecosystem, it embodies a culture driven by integrity, discovery, and partnership—values that foster innovation while maintaining rigorous standards and ethical accountability. The company’s priority to accelerate progress through technology and essential intelligence creates a fertile environment for AI-driven initiatives like the one this role supports. Candidates should anticipate a dynamic, mission-oriented culture that values intellectual curiosity, transparent collaboration, and the tangible impact of data-driven insights on economic decision-making worldwide. This environment expects technical experts not just to be coders but strategic partners capable of working closely with diverse stakeholders. Organizationally, this role is likely positioned as a senior individual contributor with significant cross-team visibility, interfacing between data science, MLOps, and business units — indicating a high-impact position that can shape technical direction and product innovation. Given S&P’s commitment to evolving complexity and sustainability, the role aligns strategically with the company’s broader goals of enhancing financial transparency and guiding sustainable economic growth. Success in this role means aligning advanced technical competencies with the company’s mission to deliver clarity from complexity, enabling confident market decisions and fostering long-term societal progress.

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

• Behavioral: Tell me about a time when you had to collaboratively troubleshoot a complex machine learning pipeline. How did you approach the problem and ensure a timely resolution?
• Behavioral: Describe an experience where you had to explain highly technical NLP or AI concepts to non-technical business stakeholders. How did you ensure understanding and buy-in?
• Behavioral: Can you share an example of when you had to balance innovation and adherence to best practices or governance standards in a model development project?
• Behavioral: Tell me about a project where you worked across multiple teams, such as data science, MLOps, and product, to deliver a machine learning solution. What challenges did you face, and how did you overcome them?
• Technical: Walk me through your approach to designing and fine-tuning a transformer-based NLP model for a domain-specific use case. How do you ensure the model meets both accuracy and efficiency requirements?
• Technical: How would you implement and evaluate a Retrieval Augmented Generation (RAG) pipeline leveraging LLMs in an enterprise context like financial risk analysis?
• Technical: What experience do you have with deploying NLP/LLM models in production environments? How do you collaborate with MLOps teams to ensure model reliability and scalability?
• Technical: Describe the techniques you use for prompt engineering and fine-tuning in generative AI models to optimize output relevance and accuracy.
• Situational: Imagine the LLM you fine-tuned suddenly exhibits degraded performance after deployment. How would you investigate and address this issue while minimizing impact to business users?
• Situational: You receive conflicting priorities from product stakeholders wanting rapid delivery and data governance teams emphasizing strict model validation. How do you navigate these competing demands?
• Situational: Suppose your model’s training dataset includes unstructured financial documents containing sensitive information. How would you approach data ingestion and preprocessing to ensure compliance and data quality?
• Company Fit/Motivation: What interests you most about applying your NLP and GenAI skills specifically at S&P Global Ratings, considering our mission and industry context?
• Company Fit/Motivation: How do you see your personal values aligning with S&P Global’s emphasis on integrity, discovery, and partnership?
• Company Fit/Motivation: Given S&P Global’s focus on sustainability and accelerating progress, how would you leverage AI advancements to contribute to these strategic goals?
• Questions to Ask Interviewer: How does the data science team at S&P Global Ratings collaborate with other divisions to innovate around AI/ML solutions?
• Questions to Ask Interviewer: What are the biggest technical challenges currently facing your NLP and LLM initiatives, and how does this role help to address them?
• Questions to Ask Interviewer: How does S&P Global support continuous learning and career development for data scientists working on cutting-edge AI technologies?