S&P Global Research Scientist Interview Questions + Guide in 2025

S&P Global is a leading provider of financial market data and analytics, facilitating informed decision-making for businesses and investors worldwide.

As a Research Scientist at S&P Global, you will be at the forefront of AI innovation, working within a dynamic team focused on machine learning and data discovery. Your primary responsibilities will include developing advanced natural language processing (NLP) models and algorithms, collaborating closely with other scientists and engineers to explore and solve complex problems. You will be tasked with conducting research, writing clean and scalable code, and contributing to a culture of excellence in design and documentation.

The ideal candidate will possess a PhD or a Master's with substantial research experience in Computer Science or a related field. They should have experience publishing in top-tier ML/NLP conferences and be proficient in programming languages such as PyTorch or TensorFlow. A strong analytical mindset, the capacity to work with large datasets, and an eagerness to collaborate are crucial traits for success in this role. At S&P Global, we value continuous learning and a supportive environment that fosters innovation, making it essential for candidates to demonstrate a proactive approach in sharing knowledge and a genuine passion for research.

This guide aims to equip you with a deeper understanding of the role and the unique demands of S&P Global, enhancing your preparation and confidence as you approach the interview process.

The interview process for a Research Scientist at S&P Global is structured and thorough, reflecting the company's commitment to finding the right talent for their innovative projects. The process typically consists of several rounds, each designed to assess different aspects of a candidate's qualifications and fit for the role.

1. Initial Screening

The first step in the interview process is an initial screening, which usually takes place via a video call. This session is typically conducted by a recruiter and lasts about 30 minutes. During this call, the recruiter will discuss your background, motivations for applying, and general fit for the company culture. They may also touch on your understanding of the role and the specific skills you bring to the table.

2. Technical Assessment

Following the initial screening, candidates often undergo a technical assessment. This may include a coding test or a series of technical questions focused on relevant skills such as machine learning, natural language processing, and programming languages like Python and SQL. The assessment is designed to evaluate your problem-solving abilities and your proficiency in handling complex data-related tasks.

3. Behavioral Interviews

Candidates typically participate in one or more behavioral interviews with team members or managers. These interviews focus on your past experiences, teamwork, and how you approach challenges. Expect questions that explore your collaborative skills, adaptability, and how you handle feedback. This is also an opportunity for you to demonstrate your passion for research and innovation.

4. In-Depth Technical Interviews

In this stage, candidates may have multiple rounds of in-depth technical interviews with senior researchers or engineers. These interviews delve deeper into your technical expertise, including discussions about your previous research, specific projects you've worked on, and your familiarity with tools and technologies used in the field. You may be asked to solve problems on the spot or discuss your approach to developing novel solutions.

5. Final Interview

The final interview often involves a discussion with higher management or a panel of interviewers. 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. It’s also a chance for you to ask questions about the team dynamics, company culture, and future projects.

Throughout the process, candidates are encouraged to showcase their research experience, particularly any publications in top-tier conferences, as well as their ability to work with large datasets and complex models.

As you prepare for your interview, consider the types of questions that may arise in each of these stages, particularly those that relate to your technical skills and collaborative experiences.

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

Understand the Company Culture

Kensho, as part of S&P Global, emphasizes a collaborative and communicative environment. Familiarize yourself with their focus on teamwork and innovation in AI and machine learning. Be prepared to discuss how your values align with their culture, particularly in terms of collaboration and continuous learning. Highlight experiences where you worked effectively in teams or contributed to a shared goal.

Prepare for Technical Proficiency

Given the emphasis on algorithms and programming skills, particularly in Python and SQL, ensure you are well-versed in these areas. Brush up on your knowledge of machine learning frameworks like PyTorch, as well as concepts related to natural language processing (NLP). Be ready to discuss your experience with large datasets and any relevant projects that demonstrate your technical capabilities.

Showcase Your Research Experience

As a Research Scientist, your ability to contribute to cutting-edge research is crucial. Be prepared to discuss your past research, particularly any publications in top-tier conferences. Highlight your familiarity with current trends in NLP and machine learning, and be ready to articulate how your work can contribute to Kensho's ongoing projects, such as long-context question-answering and complex reasoning.

Anticipate Behavioral Questions

Expect behavioral questions that assess your problem-solving abilities and teamwork. Prepare examples that demonstrate your critical thinking skills and how you handle challenges. Given the feedback from previous candidates, be ready to discuss your motivations for joining Kensho and how you envision your growth within the company.

Engage with the Interviewers

Interviews at Kensho are described as friendly and professional. Use this to your advantage by engaging with your interviewers. Ask insightful questions about their projects, team dynamics, and the company’s future direction. This not only shows your interest but also helps you gauge if the company is the right fit for you.

Be Ready for a Structured Process

The interview process may involve multiple rounds, including technical assessments and behavioral interviews. Stay organized and manage your time effectively. If you encounter any delays or lack of communication, remain patient and professional, as the process can be lengthy.

Emphasize Your Learning Mindset

Kensho values individuals who are eager to learn and share knowledge. Highlight your passion for continuous learning and any experiences where you have taken the initiative to expand your skills. Discuss how you can contribute to a culture of knowledge sharing within the team.

Prepare for Technical Assessments

Expect technical assessments that may include coding challenges or problem-solving scenarios. Practice common algorithms and data structures, and be prepared to explain your thought process clearly. Familiarize yourself with SQL queries and Python programming, as these are likely to be focal points during technical evaluations.

By following these tips and preparing thoroughly, you can present yourself as a strong candidate who aligns well with Kensho's mission and values. Good luck!

In this section, we’ll review the various interview questions that might be asked during an interview for a Research Scientist position at S&P Global's Kensho. The interview process will likely focus on your technical expertise in machine learning, natural language processing, and your ability to work collaboratively in a research environment. Be prepared to discuss your past experiences, technical skills, and how you approach problem-solving in a team setting.

Technical Skills

1. Can you explain the architecture of a neural network and how it can be applied to NLP tasks?

Understanding the fundamentals of neural networks is crucial for this role, especially in the context of NLP.

How to Answer

Discuss the layers of a neural network, including input, hidden, and output layers, and how they process data. Highlight specific architectures relevant to NLP, such as RNNs, LSTMs, or Transformers.

Example

“A neural network consists of an input layer, several hidden layers, and an output layer. For NLP tasks, architectures like Transformers have revolutionized the field by allowing for parallel processing of data and capturing long-range dependencies, which is essential for understanding context in language.”

2. What techniques do you use for tokenization in NLP?

Tokenization is a fundamental step in processing text data for NLP applications.

How to Answer

Explain different tokenization methods, such as word-level, subword-level (like Byte Pair Encoding), and character-level tokenization, and their implications on model performance.

Example

“I typically use subword tokenization methods like Byte Pair Encoding, as they help in handling out-of-vocabulary words effectively and reduce the vocabulary size, which is crucial for training large language models.”

3. Describe a project where you developed a machine learning model. What challenges did you face?

This question assesses your practical experience and problem-solving skills.

How to Answer

Outline the project scope, the model you developed, the data you used, and the challenges you encountered, such as data quality or model performance issues.

Example

“In my last project, I developed a sentiment analysis model using a large dataset of customer reviews. One challenge was dealing with imbalanced classes, which I addressed by implementing SMOTE to generate synthetic samples for the minority class, significantly improving model accuracy.”

4. How do you evaluate the performance of an NLP model?

Evaluation metrics are critical for understanding model effectiveness.

How to Answer

Discuss various metrics such as accuracy, precision, recall, F1-score, and specific metrics for NLP tasks like BLEU or ROUGE for translation tasks.

Example

“I evaluate NLP models using precision, recall, and F1-score, especially for classification tasks. For generative tasks, I use BLEU scores to assess the quality of generated text against reference texts.”

5. Can you explain the concept of attention mechanisms in neural networks?

Attention mechanisms are pivotal in modern NLP models.

How to Answer

Describe how attention mechanisms work and their role in improving model performance by allowing the model to focus on relevant parts of the input data.

Example

“Attention mechanisms allow models to weigh the importance of different words in a sentence when making predictions. This is particularly useful in tasks like translation, where the context of words can change the meaning significantly.”

Behavioral Questions

1. Describe a time when you had to work collaboratively on a research project. What was your role?

Collaboration is key in research environments.

How to Answer

Share a specific example that highlights your teamwork skills, your contributions, and how you navigated any challenges.

Example

“I worked on a team project to develop a document classification system. My role involved designing the model architecture and collaborating with data engineers to ensure data quality. We faced challenges in aligning our timelines, but regular check-ins helped us stay on track.”

2. How do you stay updated with the latest research in machine learning and NLP?

Continuous learning is essential in a rapidly evolving field.

How to Answer

Mention specific journals, conferences, or online platforms you follow, and how you apply new knowledge to your work.

Example

“I regularly read papers from conferences like ACL and NeurIPS and follow relevant blogs and podcasts. I also participate in online courses to deepen my understanding of emerging techniques, which I then share with my team during our knowledge-sharing sessions.”

3. What motivates you to work in the field of AI and machine learning?

Understanding your passion can help interviewers gauge your fit for the role.

How to Answer

Discuss your interest in AI, any personal experiences that sparked this interest, and how it aligns with your career goals.

Example

“I am motivated by the potential of AI to solve complex problems and improve lives. My interest began during my undergraduate studies when I worked on a project that used machine learning to predict health outcomes, and I’ve been passionate about this field ever since.”

4. How do you handle criticism of your work?

This question assesses your ability to accept feedback and grow.

How to Answer

Share your perspective on constructive criticism and provide an example of how you’ve used feedback to improve your work.

Example

“I view criticism as an opportunity for growth. For instance, after receiving feedback on a research paper, I took the time to revise my methodology section, which ultimately strengthened the paper and led to its acceptance at a conference.”

5. Where do you see yourself in five years?

This question helps interviewers understand your career aspirations.

How to Answer

Discuss your professional goals and how they align with the company’s mission and values.

Example

“In five years, I see myself leading a research team focused on developing innovative NLP solutions. I hope to contribute to groundbreaking projects at Kensho and mentor junior researchers, fostering a collaborative and creative environment.”