Preparation for S&P Global requires a balance of strong foundational knowledge and the ability to articulate your specific contributions to past projects. The interviewers are looking for engineers who can bridge the gap between theoretical research and production-ready code.

Focus on these key evaluation criteria:

Project Deep Dive & Ownership – You must know every detail of the projects listed on your resume. Interviewers frequently ask you to walk through a project from problem framing to deployment. You need to explain why you chose a specific model, how you handled data leakage, and what trade-offs you made.

Technical Proficiency (Python & ML Frameworks) – Expect to demonstrate fluency in Python and libraries such as PyTorch, Transformers, and Scikit-learn. You will be evaluated on your ability to write clean, efficient code, not just your ability to derive a mathematical proof.

Domain Adaptability (NLP & GenAI) – Given the focus of S&P Global’s AI division, familiarity with NLP, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) is increasingly critical. Even if your background is general ML, showing an aptitude for learning these specific technologies is essential.

Communication & Collaboration – You will often work with product managers and non-technical stakeholders. Interviewers assess whether you can explain complex "black box" models in simple, business-centric terms.

The interview process at S&P Global is thorough but can vary significantly depending on the specific team (e.g., Kensho vs. Market Intelligence) and location. generally, the process is designed to test both your coding ability and your theoretical understanding of Machine Learning. Candidates often report a process that ranges from 2 weeks to 3 months, requiring patience and proactive follow-up.

Typically, the process begins with a recruiter screen, followed by a hiring manager interview which serves as a resume deep dive and behavioral screen. If successful, you move to the technical rounds. These rounds are a mix of coding assessments (often involving LeetCode-style questions or practical data manipulation) and conceptual discussions where you are grilled on your past projects. In some regions or for campus hires, you might encounter a Group Discussion (GD) round or aptitude tests, though experienced hires typically move straight to technical one-on-ones.

The philosophy is "depth over breadth." Rather than asking you to solve ten different puzzles, interviewers prefer to take one project or one coding problem and expand on it for 45–60 minutes, adding constraints and asking for optimizations.

Understanding the timeline: The visual above outlines the standard flow. Note that the "Technical Rounds" often consist of two separate interviews: one focused on coding/algorithms and another focused on ML system design or project experience. Be prepared for potential delays between rounds; candidates have reported gaps where recruiter communication can be slow.

Based on candidate reports and job requirements, S&P Global focuses on four main pillars during the technical evaluation.

Project Experience & Resume Deep Dive

This is the most consistent part of the interview process. Interviewers will pick one project from your resume and ask you to deconstruct it. They are looking for evidence that you understand the entire lifecycle of the model, not just the training phase.

Be ready to go over:

• Problem Framing: How did you translate a business problem into a data science problem?
• Data Engineering: How did you clean, store, and preprocess the data? (Mention tools like SQL, Pandas, or AWS S3).
• Model Selection: Why did you choose XGBoost over a Neural Network, or vice versa?
• Advanced concepts: Handling class imbalance, preventing overfitting in low-data environments, and deployment strategies (Docker/Kubernetes).

Example questions or scenarios:

• "Walk me through the most challenging project on your resume. What was your specific contribution?"
• "How did you validate the results of this model? What metrics did you use and why?"
• "If you had to scale this solution to 100x the data volume, what would break first?"

Applied Machine Learning & NLP

Given the company's focus on unstructured text data, NLP is a heavy focus. Even for generalist roles, expect questions that test your understanding of modern AI architectures.

Be ready to go over:

• NLP Fundamentals: Tokenization, embeddings (Word2Vec, BERT), and text preprocessing.
• Generative AI: Transformers, Attention mechanisms, LLMs, and RAG systems.
• Classic ML: Regression, Classification, Clustering, and Dimensionality Reduction.
• Advanced concepts: Graph Neural Networks (GNNs), Agentic orchestration, and fine-tuning strategies.

Example questions or scenarios:

• "Explain the attention mechanism in Transformers to a non-technical person."
• "How would you approach extracting specific financial entities from a PDF document?"
• "What are the limitations of using RAG (Retrieval-Augmented Generation) in a financial context?"

Coding & Algorithms

You will likely face a coding round. The difficulty varies from "easy" Python scripting tasks to "medium/hard" algorithmic problems. The goal is to verify you can write production-quality code.

Be ready to go over:

• Data Structures: Arrays, Hash Maps, Linked Lists, and Trees.
• Python Specifics: List comprehensions, generators, and pandas manipulation.
• Algorithmic Logic: String manipulation (very common due to NLP focus) and optimization problems.

Example questions or scenarios:

• "Write a function to parse a complex string and return specific patterns."
• "Solve a standard LeetCode medium problem (e.g., array manipulation) on a whiteboard or shared editor."
• "Optimize this Python script to run faster on a large dataset."

Statistics & Aptitude

Particularly in early rounds or specific regional processes, you may face questions testing your mathematical intuition.

Be ready to go over:

• Probability: Bayes' theorem, distributions, and hypothesis testing.
• Aptitude: Logic puzzles or quantitative reasoning (more common in intern or junior roles).

As a Data Scientist at S&P Global, your day-to-day work balances research with engineering. You are expected to design, build, and maintain scalable ML systems. This means you aren't just handing off a Jupyter Notebook to an engineering team; you are often involved in the deployment lifecycle, partnering with MLOps teams to ensure your models run reliably in production.

A significant portion of your time will be spent on Data Discovery and Extraction. You will apply advanced NLP techniques to parse proprietary datasets, turning messy financial documents into structured insights. You will likely work within a cross-functional team comprising Product Managers, Backend Engineers, and Designers. Collaboration is key; you will participate in technical discussions, code reviews, and "deep dives" to solve hard problems using diverse perspectives.

Innovation is a core responsibility. You will experiment with Agentic systems and GenAI workflows, exploring new ideas while rooting them in engineering best practices. Whether it is improving an existing data retrieval API or building a new LLM tool for internal use, your work drives the company's technological edge.