S&P Global Machine Learning Engineer Interview Questions + Guide in 2025

S&P Global is a leading provider of credit ratings, benchmarks, analytics, and workflow solutions, helping organizations navigate the complexities of the economic landscape.

As a Machine Learning Engineer at S&P Global, you will be part of Kensho, the company's hub for AI innovation and transformation. This role involves designing, building, and maintaining scalable production-ready machine learning systems, primarily working with large datasets to derive actionable insights. Your responsibilities will include conducting original work on both structured and unstructured data, applying advanced machine learning techniques, and collaborating with cross-functional teams to integrate machine learning models into production systems. You will also play a key role in the machine learning model lifecycle, from problem framing and training to deployment and monitoring.

To excel in this position, candidates should possess a solid foundation in machine learning principles, experience with Python and ML frameworks (such as PyTorch or TensorFlow), and strong communication skills to articulate complex models and results to diverse audiences. A collaborative spirit and the ability to thrive in a team-oriented environment are essential traits that align with S&P Global's values of integrity, discovery, and partnership.

This guide aims to equip you with the insights and knowledge necessary to prepare effectively for your interview, enabling you to confidently showcase your skills and fit for the role at S&P Global.

The interview process for a Machine Learning Engineer at S&P Global is structured to assess both technical skills and cultural fit within the organization. It typically consists of several key stages:

1. Initial Screening

The process begins with an initial screening, which is usually a phone call with a recruiter or a hiring manager. This conversation is primarily focused on understanding your background, experiences, and motivations for applying to S&P Global. Expect to discuss your previous work, relevant projects, and how your skills align with the role. This is also an opportunity for you to ask questions about the company culture and the specifics of the position.

2. Technical Assessment

Following the initial screening, candidates typically undergo a technical assessment. This may involve a coding challenge or a technical interview conducted via video conferencing. During this stage, you will be evaluated on your proficiency in programming languages, particularly Python, and your understanding of machine learning concepts and frameworks. You may be asked to solve problems related to data manipulation, model building, and algorithm implementation, as well as discuss your approach to machine learning projects.

3. Behavioral Interview

After the technical assessment, candidates usually participate in a behavioral interview. This round focuses on assessing your soft skills, teamwork, and how you handle challenges in a collaborative environment. Expect questions that explore your past experiences in team settings, your problem-solving strategies, and how you communicate complex technical concepts to non-technical stakeholders. This is a crucial part of the process, as S&P Global values strong communication and collaboration skills.

4. Final Interview

The final interview often involves meeting with senior team members or leadership. This round may include a mix of technical and behavioral questions, as well as discussions about your long-term career goals and how they align with the company’s vision. You may also be asked to present a project or a case study that showcases your skills and thought process in machine learning.

5. Offer and Negotiation

If you successfully navigate the previous stages, you will receive a job offer. This stage may involve discussions about salary, benefits, and other employment terms. S&P Global is known for its competitive compensation packages, so be prepared to negotiate based on your experience and the market standards.

As you prepare for your interview, consider the specific skills and experiences that align with the role, as well as the collaborative culture at S&P Global. Next, let’s delve into the types of questions you might encounter during the interview process.

Here are some tips to help you excel in your interview.

Understand the Role's Focus

While the title may suggest a traditional Machine Learning Engineer position, be aware that this role leans more towards data engineering and software engineering. Familiarize yourself with the responsibilities of building data pipelines and visualization tools, as these will be central to your work. Highlight your experience in these areas during the interview, and be prepared to discuss how your skills can contribute to the team’s objectives.

Emphasize Collaboration and Communication

Kensho values a collaborative environment where diverse perspectives are leveraged to solve complex problems. Be ready to share examples of how you have successfully worked in cross-functional teams. Additionally, since you may need to explain complex technical concepts to non-technical stakeholders, practice articulating your thoughts clearly and concisely. This will demonstrate your ability to bridge the gap between technical and non-technical team members.

Prepare for Behavioral Questions

Given the company culture that emphasizes teamwork and communication, expect behavioral questions that assess your interpersonal skills and adaptability. Use the STAR (Situation, Task, Action, Result) method to structure your responses, focusing on specific instances where you demonstrated collaboration, problem-solving, or leadership.

Showcase Your Technical Proficiency

While the role may not focus heavily on model training, a solid understanding of machine learning principles and tools is still essential. Be prepared to discuss your experience with relevant technologies such as Python, PyTorch, and data management tools like Apache Spark and AWS. Highlight any projects where you have applied these technologies to solve real-world problems, as this will showcase your practical experience.

Inquire About Growth Opportunities

Although feedback suggests limited growth potential in this role, expressing a desire for professional development can set you apart. Ask about opportunities for learning and advancement within the company. This shows your commitment to personal growth and your interest in contributing to the organization in the long term.

Leverage the Company Culture

Kensho promotes a tightly-knit community and values in-person collaboration. If you have experience working in similar environments, share those insights. Discuss how you thrive in collaborative settings and how you can contribute to maintaining a positive team dynamic. This will resonate well with the interviewers and align with their cultural values.

Be Ready for a Compressed Work Schedule

The company offers a four-day work week, which is a unique perk. Be prepared to discuss how you would manage your time effectively within this structure. This can demonstrate your ability to prioritize tasks and maintain productivity, which is crucial in a compressed work environment.

By focusing on these tailored strategies, you can present yourself as a well-rounded candidate who not only possesses the necessary technical skills but also aligns with the company’s culture and values. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at S&P Global. The interview will likely focus on your technical skills in machine learning, data engineering, and your ability to work collaboratively in a team environment. Be prepared to discuss your experience with various tools and technologies, as well as your approach to problem-solving in machine learning contexts.

Machine Learning and Data Engineering

1. Can you describe your experience with building and deploying machine learning models?

This question aims to assess your practical experience in the machine learning lifecycle, from model development to deployment.

How to Answer

Discuss specific projects where you were involved in the end-to-end process of machine learning, including data preprocessing, model selection, training, and deployment. Highlight any challenges you faced and how you overcame them.

Example

“In my previous internship, I developed a predictive model for customer churn using Python and scikit-learn. I handled data preprocessing, feature engineering, and model training. After achieving a satisfactory accuracy, I deployed the model using Docker and monitored its performance in production, making adjustments as necessary.”

2. What techniques do you use for feature selection and engineering?

This question evaluates your understanding of the importance of features in model performance.

How to Answer

Explain the methods you use for feature selection, such as correlation analysis, recursive feature elimination, or using algorithms like LASSO. Discuss how you engineer features to improve model performance.

Example

“I often start with correlation analysis to identify features that have a strong relationship with the target variable. I also use recursive feature elimination to systematically remove less important features. For feature engineering, I create new features based on domain knowledge, such as aggregating transaction data to derive customer behavior metrics.”

3. How do you ensure the scalability of your machine learning solutions?

This question assesses your ability to design systems that can handle increasing amounts of data and user requests.

How to Answer

Discuss your experience with scalable architectures, such as using cloud services, distributed computing, or containerization. Mention any specific tools or frameworks you have used.

Example

“I ensure scalability by leveraging cloud platforms like AWS for storage and computation. For instance, I used AWS S3 for data storage and AWS Lambda for serverless computing, which allowed my model to handle varying loads efficiently. Additionally, I implemented a microservices architecture to separate different components of the application, making it easier to scale individual services.”

Natural Language Processing (NLP)

4. What experience do you have with NLP techniques and tools?

This question focuses on your familiarity with NLP methodologies and frameworks.

How to Answer

Mention specific NLP tasks you have worked on, such as text classification, named entity recognition, or sentiment analysis. Highlight the tools and libraries you have used.

Example

“I have worked on several NLP projects, including a sentiment analysis tool using the Hugging Face Transformers library. I fine-tuned a BERT model on a custom dataset to classify customer reviews. I also utilized NLTK for text preprocessing tasks like tokenization and stemming.”

5. Can you explain the concept of word embeddings and their importance in NLP?

This question tests your understanding of foundational NLP concepts.

How to Answer

Define word embeddings and explain how they capture semantic relationships between words. Discuss their advantages over traditional methods like one-hot encoding.

Example

“Word embeddings are dense vector representations of words that capture their meanings based on context. They allow models to understand semantic relationships, such as synonyms and analogies, which is crucial for tasks like sentiment analysis and machine translation. Unlike one-hot encoding, embeddings reduce dimensionality and improve model performance.”

Collaboration and Communication

6. Describe a situation where you had to work with a cross-functional team. How did you ensure effective communication?

This question evaluates your teamwork and communication skills.

How to Answer

Share an example of a project where you collaborated with team members from different disciplines. Highlight how you facilitated communication and ensured everyone was aligned.

Example

“During a project to develop a recommendation system, I collaborated with data scientists, product managers, and software engineers. I organized regular stand-up meetings to discuss progress and challenges, and I created a shared documentation space to keep everyone updated on technical decisions and project milestones.”

7. How do you approach explaining complex technical concepts to non-technical stakeholders?

This question assesses your ability to communicate effectively with diverse audiences.

How to Answer

Discuss your strategies for simplifying complex ideas, such as using analogies, visual aids, or focusing on the business impact of technical decisions.

Example

“When presenting to non-technical stakeholders, I focus on the business implications of our work rather than the technical details. For instance, when explaining a machine learning model, I use visualizations to show how it improves decision-making and provide concrete examples of its impact on revenue or customer satisfaction.”