Interview Guides

S&P Global Data Scientist Interview Guide 2026

S&P Global

Data Scientist

What is a Data Scientist at S&P Global?

At S&P Global, the Data Scientist role is pivotal to the company's evolution from a traditional financial information provider to a technology-first data intelligence firm. Much of this innovation is driven by Kensho, S&P Global’s hub for AI and transformation. In this position, you are not just analyzing static datasets; you are often building the engines that structure the world's financial data. You will work on cutting-edge solutions involving Generative AI, Natural Language Processing (NLP), and Agentic systems to extract insights from massive repositories of unstructured and structured data.

The impact of this role is high-visibility and strategic. You will develop models that directly power products used by global financial institutions, governments, and corporations. Whether you are working on LLM-powered applications, data retrieval APIs, or fundamental AI toolkits like Kensho Extract, your work ensures that S&P Global’s customers can make decisions with speed and precision. You will join a collaborative environment—often described as the "Kenshin" community—where autonomy is high, and engineering best practices are strictly followed to ensure scalability and robustness.

Common Interview Questions

The following questions are representative of what candidates have faced at S&P Global. They cover technical theory, practical coding, and behavioral fit. Note that questions can change based on the specific team (e.g., Kensho vs. Ratings).

Technical & Machine Learning

These questions test your depth of knowledge in the field.

"Explain the difference between Bagging and Boosting."
"How do you handle overfitting in a neural network?"
"What is the difference between Word2Vec and BERT embeddings?"
"How would you design a system to classify financial news sentiment?"
"Explain the concept of 'Attention' in the context of NLP."

Coding & Problem Solving

Expect these to be solved in Python, often on a whiteboard or shared screen.

"Given a list of strings, group the anagrams together."
"Write a function to reverse a string without using built-in reverse functions."
"Find the missing number in an array of integers."
"Implement a basic calculator that handles parentheses."

Behavioral & Project Experience

These determine your cultural fit and ability to deliver.

"Tell me about a time you had to learn a new technology quickly to solve a problem."
"Describe a conflict you had with a team member and how you resolved it."
"Walk me through a project where you failed or hit a major roadblock. How did you handle it?"
"Why do you want to work at S&P Global/Kensho specifically?"

See every interview question for this role

Practice questions from our question bank

Curated questions for S&P Global from real interviews. Click any question to practice and review the answer.

Easy

Model Evaluation

Explain Precision vs Recall

Explain why a pneumonia classifier with 91% precision but 68% recall may still be unsafe, and recommend which metric to prioritize.

Precision

Recall

F1 Score

Easy

Pipelines

Handle Missing Values in ETL

Design a batch ETL pipeline that detects, imputes, and monitors missing values before loading analytics tables with daily SLA compliance.

ETL

Data Wrangling

Quality

Easy

Model Evaluation

Interpret F1 for Imbalanced Classification

Explain why F1 is more informative than accuracy for a fraud model with 97.2% accuracy but only 18% recall on a 1% positive class.

Precision

Recall

F1 Score

Easy

SQL & Data Manipulation

Handling Missing Values in SQL

Explain how to detect and handle NULL values in SQL using filtering, COALESCE, CASE, and business-aware imputation.

Aggregations

Case When

Data Wrangling

Easy

Model Evaluation

Explain Cross-Validation to Executives

Explain why cross-validation gives a more trustworthy view of model performance than a single strong test split.

Cross-Validation

Accuracy

Calibration

Easy

Model Evaluation

Compare Precision-Recall Tradeoffs

Compare two classifiers with high-precision vs high-recall behavior and recommend the better model under business cost and review-capacity constraints.

Precision

Recall

F1 Score

Medium

Model Evaluation

Evaluate F1 Score Significance in Model Performance

Analyze the significance of the F1 score in a binary classification model for customer churn prediction, and propose improvements.

Accuracy

F1 Score

Easy

Model Evaluation

Choose RMSE vs MAE

Compare two rent prediction models and decide whether MAE or RMSE is the better selection metric given costly large errors.

Regression

RMSE

MAE

Easy

Pipelines

Build Data Quality Controls Pipeline

Design a batch ETL pipeline that validates CRM, billing, and product data before loading curated Snowflake tables.

Data Modeling

ETL

Quality

Easy

SQL & Data Manipulation

Classify Orders with CASE WHEN

Explain how CASE WHEN adds conditional logic to SQL queries for labeling, transforming, and aggregating data.

Aggregations

Case When

Data Wrangling

Easy

Pipelines

Ensure Data Quality in ETL

Design a Snowflake ETL pipeline that enforces schema, deduplication, reconciliation, and auditable data quality checks for finance data.

Data Modeling

ETL

Quality

Hard

NLP

Explain Transformer Architecture and Attention Mechanisms

Discuss the architecture of Transformers, focusing on self-attention and its impact on NLP tasks.

Neural Networks

Language Models

Deep Learning

Medium

Model Evaluation

Evaluate Model Metrics for Customer Churn Prediction

Analyze why a customer churn prediction model has low recall despite high precision and propose actionable improvements.

Easy

Metrics

Measure Checkout Funnel Conversion Rate

Define overall and step-level funnel conversion for an e-commerce checkout flow and explain how to diagnose where drop-off occurs.

KPIs

Conversion Rate

Funnel Analysis

Easy

SQL & Data Manipulation

Handling Missing Demographic Data

Explain how to assess, quantify, and handle missing demographic fields in SQL without distorting downstream analysis.

Subqueries

Case When

Data Wrangling

Easy

SQL & Data Manipulation

Detect and Handle Outliers in SQL

Explain common SQL-friendly ways to detect outliers and how to handle them without distorting downstream analysis.

Aggregations

Group By

Data Wrangling

Easy

Machine Learning

Compare Bagging and Boosting for Claims Risk

Explain and compare bagging vs boosting by training tree-based ensembles to predict high-cost insurance claims.

Ensemble Methods

Bias-Variance Tradeoff

Decision Trees

Easy

Statistics & Probability

A/B Test for Ranking Model Value

Use a two-proportion z-test to determine whether a new ranking model significantly improves recommendation CTR in an A/B test.

A/B Testing

Statistical Significance

Experimentation

Easy

Model Evaluation

Choose Metrics for Business Impact

Decide whether precision, recall, F1-score, or RMSE best fits fraud detection and demand forecasting given asymmetric business costs.

Accuracy

Precision

Recall

+2 more

Medium

Product Sense

Investigate Engagement Drop with Stable ARR

Analyze the root cause of a 10% engagement drop while ARR remains flat to inform product strategy.

User Needs

Engagement Metrics

Churn

+2 more

Sign up to see all questions

Create a free account to access every interview question for this role.

Getting Ready for Your Interviews

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.

Interview Process Overview

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.

Deep Dive into Evaluation Areas

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).

Key Responsibilities

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.

Role Requirements & Qualifications

S&P Global looks for candidates who are technically versatile and academically grounded. While they value diverse backgrounds, the following profile is typical for successful candidates.

Technical Skills

Must-have: Strong expertise in Python and ML frameworks (PyTorch, Scikit-Learn, Transformers). Experience with data manipulation libraries (Pandas, NumPy).
Core ML Knowledge: Deep understanding of NLP, Large Language Models (LLMs), and statistical modeling.
Engineering Practices: Familiarity with version control (Git), containerization (Docker), and cloud platforms (AWS).
Nice-to-have: Experience with Graph Neural Networks (GNNs), LangGraph, Vector Databases (Pgvector, OpenSearch), and MLOps tools (Airflow, Jenkins).

Experience Level

Education: Pursuing or holding a Bachelor’s, Master’s, or PhD in Computer Science, Mathematics, Statistics, or a related field.
Professional Experience: For non-intern roles, prior experience deploying ML models to production is highly valued. Interns are expected to have relevant coursework and project experience.

Soft Skills

Communication: Ability to express complicated methods to broad audiences.
Autonomy: A self-starter attitude with a passion for solving unstructured problems.
Collaboration: Willingness to work in a tightly-knit, feedback-rich environment.

Frequently Asked Questions

Q: How difficult are the coding interviews? The difficulty ranges from Easy to Medium-Hard. While some candidates report simple Python scripting questions, others face standard LeetCode Medium algorithmic problems. It is best to prepare for Medium-level difficulty, specifically focusing on string manipulation and array handling.

Q: Does S&P Global offer remote work for Data Scientists? It depends on the team. The job postings for Kensho (the AI hub) emphasize "in-person collaboration" and require interns and employees to work out of hubs like Cambridge, MA or New York City. However, hybrid arrangements are common for many full-time roles.

Q: How long does the hiring process take? The process can be lengthy. Reports vary from 2 weeks to 3 months. Delays between the recruiter screen and the final offer are common. If you haven't heard back after a week, it is acceptable to follow up politely.

Q: Is the role more research-oriented or engineering-oriented? It is a hybrid, but with a strong lean toward engineering. You are expected to build "production-ready" systems. Pure research roles exist but are rarer; most Data Scientists are expected to write robust code that integrates with company products.

Q: What is the "Kensho" distinction? Kensho is the innovation/AI arm of S&P Global. If you are interviewing for a role within Kensho, expect a startup-like culture, higher technical bars for GenAI/NLP, and a faster-paced environment compared to traditional corporate roles.

Other General Tips

Know your "Why": S&P Global is a data company. When asked "Why S&P?", focus on the unique opportunity to work with high-value, proprietary financial data that isn't available anywhere else. Mentioning their specific AI initiatives (like Kensho) shows you have done your homework.

Prepare for "Ghosting" Risks: Several candidates have reported disorganized communication or long gaps.

Note

Do not assume silence means rejection. The HR process can be slow. Maintain a log of your interactions and follow up professionally every 5-7 business days if you haven't heard back.

Refresh your NLP: Even if your background is in Computer Vision or Tabular data, S&P Global deals heavily in text.

Tip

Reviewing concepts like Tokenization, RAG, and Transformer architectures is highly recommended before your technical rounds, as these are core to the company's current strategy.

Be Honest About Skills: If you don't know a specific algorithm, admit it and explain how you would find the answer. Interviewers value "learning ability" highly, as noted in multiple positive interview experiences.

Code on a Whiteboard: Some interviews involve whiteboard coding (or virtual equivalent). Practice writing code without an IDE to ensure your syntax is solid.

Summary & Next Steps

Becoming a Data Scientist at S&P Global is an opportunity to work at the forefront of Financial AI. You will be challenged to apply modern Generative AI and NLP techniques to solve real-world problems that impact global markets. The role demands a blend of strong engineering skills, statistical intuition, and the ability to communicate complex ideas effectively.

To succeed, focus your preparation on Python coding fundamentals, NLP architectures, and a deep, articulate understanding of your past projects. The process may be rigorous and occasionally slow, but the result is a career in a high-impact, innovative environment. Approach your interviews with confidence, curiosity, and a readiness to learn.

Interpreting the Data: The salary figures provided above reflect the competitive nature of the role, particularly for positions within the Kensho division or major tech hubs. Compensation often includes a base salary, performance bonus, and stock options, which can vary significantly based on location (e.g., Cambridge/NYC vs. other regions) and experience level. Ensure you discuss the total compensation package with your recruiter early in the process.

See every interview question for this role

S&P Global

Data Scientist

What is a Data Scientist at S&P Global?

Common Interview Questions

Technical & Machine Learning

These questions test your depth of knowledge in the field.

"Explain the difference between Bagging and Boosting."
"How do you handle overfitting in a neural network?"
"What is the difference between Word2Vec and BERT embeddings?"
"How would you design a system to classify financial news sentiment?"
"Explain the concept of 'Attention' in the context of NLP."

Coding & Problem Solving

Expect these to be solved in Python, often on a whiteboard or shared screen.

"Given a list of strings, group the anagrams together."
"Write a function to reverse a string without using built-in reverse functions."
"Find the missing number in an array of integers."
"Implement a basic calculator that handles parentheses."

Behavioral & Project Experience

These determine your cultural fit and ability to deliver.

"Tell me about a time you had to learn a new technology quickly to solve a problem."
"Describe a conflict you had with a team member and how you resolved it."
"Walk me through a project where you failed or hit a major roadblock. How did you handle it?"
"Why do you want to work at S&P Global/Kensho specifically?"

See every interview question for this role

Practice questions from our question bank

Curated questions for S&P Global from real interviews. Click any question to practice and review the answer.

Easy

Model Evaluation

Explain Precision vs Recall

Explain why a pneumonia classifier with 91% precision but 68% recall may still be unsafe, and recommend which metric to prioritize.

Precision

Recall

F1 Score

Easy

Pipelines

Handle Missing Values in ETL

Design a batch ETL pipeline that detects, imputes, and monitors missing values before loading analytics tables with daily SLA compliance.

ETL

Data Wrangling

Quality

Easy

Model Evaluation

Interpret F1 for Imbalanced Classification

Explain why F1 is more informative than accuracy for a fraud model with 97.2% accuracy but only 18% recall on a 1% positive class.

Precision

Recall

F1 Score

Easy

SQL & Data Manipulation

Handling Missing Values in SQL

Explain how to detect and handle NULL values in SQL using filtering, COALESCE, CASE, and business-aware imputation.

Aggregations

Case When

Data Wrangling

Easy

Model Evaluation

Explain Cross-Validation to Executives

Explain why cross-validation gives a more trustworthy view of model performance than a single strong test split.

Cross-Validation

Accuracy

Calibration

Easy

Model Evaluation

Compare Precision-Recall Tradeoffs

Compare two classifiers with high-precision vs high-recall behavior and recommend the better model under business cost and review-capacity constraints.

Precision

Recall

F1 Score

Medium

Model Evaluation

Evaluate F1 Score Significance in Model Performance

Analyze the significance of the F1 score in a binary classification model for customer churn prediction, and propose improvements.

Accuracy

F1 Score

Easy

Model Evaluation

Choose RMSE vs MAE

Compare two rent prediction models and decide whether MAE or RMSE is the better selection metric given costly large errors.

Regression

RMSE

MAE

Easy

Pipelines

Build Data Quality Controls Pipeline

Design a batch ETL pipeline that validates CRM, billing, and product data before loading curated Snowflake tables.

Data Modeling

ETL

Quality

Easy

SQL & Data Manipulation

Classify Orders with CASE WHEN

Explain how CASE WHEN adds conditional logic to SQL queries for labeling, transforming, and aggregating data.

Aggregations

Case When

Data Wrangling

Easy

Pipelines

Ensure Data Quality in ETL

Design a Snowflake ETL pipeline that enforces schema, deduplication, reconciliation, and auditable data quality checks for finance data.

Data Modeling

ETL

Quality

Hard

NLP

Explain Transformer Architecture and Attention Mechanisms

Discuss the architecture of Transformers, focusing on self-attention and its impact on NLP tasks.

Neural Networks

Language Models

Deep Learning

Medium

Model Evaluation

Evaluate Model Metrics for Customer Churn Prediction

Analyze why a customer churn prediction model has low recall despite high precision and propose actionable improvements.

Easy

Metrics

Measure Checkout Funnel Conversion Rate

Define overall and step-level funnel conversion for an e-commerce checkout flow and explain how to diagnose where drop-off occurs.

KPIs

Conversion Rate

Funnel Analysis

Easy

SQL & Data Manipulation

Handling Missing Demographic Data

Explain how to assess, quantify, and handle missing demographic fields in SQL without distorting downstream analysis.

Subqueries

Case When

Data Wrangling

Easy

SQL & Data Manipulation

Detect and Handle Outliers in SQL

Explain common SQL-friendly ways to detect outliers and how to handle them without distorting downstream analysis.

Aggregations

Group By

Data Wrangling

Easy

Machine Learning

Compare Bagging and Boosting for Claims Risk

Explain and compare bagging vs boosting by training tree-based ensembles to predict high-cost insurance claims.

Ensemble Methods

Bias-Variance Tradeoff

Decision Trees

Easy

Statistics & Probability

A/B Test for Ranking Model Value

Use a two-proportion z-test to determine whether a new ranking model significantly improves recommendation CTR in an A/B test.

A/B Testing

Statistical Significance

Experimentation

Easy

Model Evaluation

Choose Metrics for Business Impact

Decide whether precision, recall, F1-score, or RMSE best fits fraud detection and demand forecasting given asymmetric business costs.

Accuracy

Precision

Recall

+2 more

Medium

Product Sense

Investigate Engagement Drop with Stable ARR

Analyze the root cause of a 10% engagement drop while ARR remains flat to inform product strategy.

User Needs

Engagement Metrics

Churn

+2 more

Sign up to see all questions

Create a free account to access every interview question for this role.

Getting Ready for Your Interviews

Focus on these key evaluation criteria:

Interview Process Overview

Deep Dive into Evaluation Areas

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

Project Experience & Resume Deep Dive

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).

Key Responsibilities

Role Requirements & Qualifications

S&P Global looks for candidates who are technically versatile and academically grounded. While they value diverse backgrounds, the following profile is typical for successful candidates.

Technical Skills

Must-have: Strong expertise in Python and ML frameworks (PyTorch, Scikit-Learn, Transformers). Experience with data manipulation libraries (Pandas, NumPy).
Core ML Knowledge: Deep understanding of NLP, Large Language Models (LLMs), and statistical modeling.
Engineering Practices: Familiarity with version control (Git), containerization (Docker), and cloud platforms (AWS).
Nice-to-have: Experience with Graph Neural Networks (GNNs), LangGraph, Vector Databases (Pgvector, OpenSearch), and MLOps tools (Airflow, Jenkins).

Experience Level

Education: Pursuing or holding a Bachelor’s, Master’s, or PhD in Computer Science, Mathematics, Statistics, or a related field.
Professional Experience: For non-intern roles, prior experience deploying ML models to production is highly valued. Interns are expected to have relevant coursework and project experience.

Soft Skills

Communication: Ability to express complicated methods to broad audiences.
Autonomy: A self-starter attitude with a passion for solving unstructured problems.
Collaboration: Willingness to work in a tightly-knit, feedback-rich environment.

Frequently Asked Questions

Other General Tips

Prepare for "Ghosting" Risks: Several candidates have reported disorganized communication or long gaps.

Note

Do not assume silence means rejection. The HR process can be slow. Maintain a log of your interactions and follow up professionally every 5-7 business days if you haven't heard back.

Refresh your NLP: Even if your background is in Computer Vision or Tabular data, S&P Global deals heavily in text.

Tip

Reviewing concepts like Tokenization, RAG, and Transformer architectures is highly recommended before your technical rounds, as these are core to the company's current strategy.

Code on a Whiteboard: Some interviews involve whiteboard coding (or virtual equivalent). Practice writing code without an IDE to ensure your syntax is solid.