S&P Global Data Analyst Interview Questions + Guide in 2024

S&P Global is a leading provider of essential intelligence that powers decision-making in a wide range of industries, including finance, automotive, and commodities. The company operates with a mission to transform data into actionable insights, facilitating progress and unlocking opportunities for its clients worldwide.

If you are aiming to join S&P Global as a Data Analyst, this guide will navigate you through the interview process, common S&P Global data analyst interview questions, and tips to help you succeed. Let’s dive in!

The interview process usually depends on the role and seniority, however, you can expect the following on an S&P Global data analyst interview:

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the S&P Global Talent Acquisition Team will make contact and verify key details like your experiences, qualifications, and skill level. Behavioral questions may also be a part of the screening process.

A few candidates have reported negative experiences with the demeanor of the interviewer, but this isn’t representative of the entire recruiting team. Be prepared to discuss your development goals, fit for the company, and preference for the role location. The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. The technical screening for the S&P Global data analyst role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around statistical methods, probability, and ETL pipelines.

Depending on the job specifics, some roles might also require knowledge in accounting, finance basics, and other financial subjects. Fresher candidates are expected to know the basics. Some rounds might include practical questions, case scenarios, and a technical exam.

Pymetric and Aptitude Tests

At S&P Global, the next stage could involve Pymetric games, followed by an aptitude test. These evaluations are used to assess your cognitive and decision-making skills, and the aptitude test includes sections on general awareness, economics, business world, and accounting basics.

Onsite Interview Rounds

If you clear the earlier stages, you’ll be invited to attend the onsite interview loops. Multiple interview rounds, varying with the role, will be conducted during your day at the S&P Global office. These include:

• Group Discussions (for some roles)
• Personal interviews with multiple managers
• Possibly case scenarios to analyze stocks or financial metrics

Some candidates have reported having final panel interviews, including with company executives. The interviews typically include competency-based questions along with practical and role-specific queries. Depending on the office or role, the level of intensity may vary.

Receiving an Offer

If you successfully navigate through all stages, you’ll move to the offer/discussion about salary and the joining date. During this stage, you may talk about compensation expectations and offer details.

Typically, interviews at S&P Global vary by role and team, but commonly Data Analyst interviews follow a fairly standardized process across these question topics.

1. What are the Z and t-tests, and when should you use each?

Explain the purpose and differences between Z-tests and t-tests. Describe scenarios where one test is preferred over the other.

2. How would you reformat student test score data for better analysis?

Given two datasets of student test scores, identify drawbacks in their current organization. Suggest formatting changes to improve usability and discuss common issues in messy datasets.

3. What metrics would you use to evaluate the value of marketing channels?

Given data on marketing channels and costs for a B2B analytics dashboard company, identify key metrics to determine the value of each marketing channel.

4. How would you determine the next partner card using customer spending data?

With access to customer spending data, outline a method to identify the best partner for a new credit card offering.

5. How would you investigate if a redesigned email campaign led to increased conversion rates?

Analyze a scenario where a new email campaign coincides with an increase in conversion rates. Determine how to investigate whether the campaign caused the increase or if other factors were involved.

6. How would you use linear interpolation to fill missing temperature data in a time-series dataframe?

A climate research organization has a time-series dataframe with daily temperature readings for different cities. Some temperature readings are missing. Write a Python function using Pandas to perform linear interpolation to estimate and fill the missing data. Ensure the interpolation considers data from the same city only. Assume no consecutive missing data and valid temperature data at the start and end of the dataframe.

7. How does random forest generate the forest? Additionally, why would we use it over other algorithms such as logistic regression?

Explain the process of how random forest generates multiple decision trees to form a forest. Discuss the advantages of using random forest over logistic regression, such as handling non-linear data and reducing overfitting.

8. Let’s say we’re comparing two machine learning algorithms. In which case would you use a bagging algorithm versus a boosting algorithm?

Compare two machine learning algorithms. Describe scenarios where bagging (e.g., Random Forest) is preferred for reducing variance and boosting (e.g., AdaBoost) is preferred for reducing bias. Provide examples of tradeoffs between the two.

9. Let’s say you work for a bank that gives out personal loans. Your co-worker develops a model that takes in customer inputs and returns if a loan should be given or not.

1. What kind of model did the co-worker develop?
2. Another co-worker thinks they have developed a better model to predict defaults on the loans. Given that personal loans are monthly installments of payments, how would you measure the difference between the two credit risk models within a timeframe?
3. What metrics would you track to measure the success of the new model?

10. What’s the difference between Lasso and Ridge Regression?

Explain the key differences between Lasso and Ridge Regression, focusing on their regularization techniques (L1 for Lasso and L2 for Ridge) and their impact on feature selection and model complexity.

11. What are the key differences between classification models and regression models?

Describe the fundamental differences between classification models (predicting categorical outcomes) and regression models (predicting continuous outcomes). Discuss their use cases and evaluation metrics.

You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your S&P Global data analyst interview include:

• Know Your Basics: You should have a good grasp of the fundamentals in accounting, financial subjects, and data management systems. Review topics such as the accounting equation, financial statements, and basic SQL.
• Prepare for Behavioral Questions: Be ready for questions about your daily life, past experience, strengths and weaknesses, compensation expectations, and reasons for switching jobs.
• Demonstrate Technical Proficiency: Proficiency in statistical methods, probability, data analysis tools such as Excel, Python, and SQL can be crucial. Prepare to discuss case scenarios that involve practical analysis and problem-solving.

What is the average salary for a Data Analyst at S&P Global?

According to Glassdoor, Data Analyst at S&P Global earn between $69K to $101K per year, with an average of $83K per year.

What kind of technical skills are required for the Data Analyst position at S&P Global

Strong proficiency in data analysis tools like Excel and Power BI is essential. Experience in coding and scripting using languages like Python, SQL, and R is highly valued. Knowledge of data visualization software, such as Tableau, can also be beneficial.

What is the company culture like at S&P Global?

S&P Global has a collaborative and fast-paced work environment. The company values diversity, equity, and inclusion, ensuring that all employees feel empowered to bring their whole selves to work. The culture emphasizes curiosity, innovation, and a commitment to delivering essential intelligence that powers decision-making.

What are the main responsibilities of a Data Analyst at S&P Global?

Responsibilities include developing and implementing databases, analyzing data trends, creating and maintaining dashboards, supporting the development of new data assets, and collaborating with various teams to enhance data quality and integration.

As the evolving data landscape constantly reshapes our world, S&P Global is looking for dedicated and curious data analysts ready to make a significant impact. Understanding your technical and analytical skills, aligning them with the company’s values, and preparing for behavioral and technical questions will set you apart. Highlight your ability to interpret and convert data into actionable business decisions and showcase your proficiency in tools like Excel, Python, and SQL.

If you want more insights about the company, check out our main S&P Global Interview Guide, where we have covered many interview questions that could be asked. Additionally, explore our interview guides for other roles such as data engineer and business analyst to learn more about S&P Global’s interview process for different positions.

Good luck with your interview!