London Stock Exchange Group Data Analyst Interview Questions + Guide in 2024

LSEG (London Stock Exchange Group) is a globally renowned financial markets infrastructure and data provider, offering unrivaled expertise and access to international capital markets. With over 300 years of history, LSEG is synonymous with trust and excellence in capital formation, intellectual property, and risk management.

Thinking of joining LSEG? This guide is designed to prepare you for the journey, offering insights into the interview process, commonly asked London Stock Exchange Group Data Analyst interview questions, and valuable tips. Let’s get started!

The interview process usually depends on the role and seniority. However, you can expect the following on a London Stock Exchange Group interview:

Recruiter/Hiring Manager Call Screening

If your CV is among the shortlisted few, a recruiter from the LSEG Talent Acquisition Team will contact you and verify key details like your experiences and skill level. Behavioral questions may also be part of the screening process.

Sometimes, the LSEG Data Analyst hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Aptitude Test

Selected candidates will be invited to take an online aptitude test. The test usually comprises 30 multiple-choice questions (MCQs) to be completed in 30 minutes. It includes questions based on logic, problem-solving, and pseudocode.

Technical Virtual Interview

Successfully navigating the recruiter round and aptitude test will invite you to the technical screening round. Technical screening for the LSEG Data Analyst role is usually conducted through virtual means, including video conference and screen sharing. Questions in this one-hour interview stage may revolve around LSEG’s data systems, ETL pipelines, and SQL queries.

Expect take-home assignments regarding product metrics, analytics, and data visualization. In addition, your proficiency in hypothesis testing, probability distributions, and Excel formulas like VLOOKUP and pivot tables may also be assessed during the round.

Onsite Interview Rounds

Following a second recruiter call outlining the next stage, you can attend the onsite interview loop. Multiple interview rounds will be conducted during your day at the LSEG office, varying with the role. These interviews will involve discussions with HR, team leaders, and peers. Your technical prowess, including programming and data management capabilities, will be evaluated against finalized candidates throughout these interviews.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the Data Analyst role at LSEG.

Typically, interviews at LSEG (London Stock Exchange Group) vary by role and team, but commonly Data Analyst interviews follow a fairly standardized process across these question topics.

1. How does random forest generate the forest and why use it over 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.

2. When 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.

3. How would you evaluate and compare two credit risk models for personal loans?

1. Identify the type of model developed by the co-worker for loan approval.
2. Describe how to measure the difference between two credit risk models over a timeframe, considering monthly installment payments.
3. List metrics to track the success of the new model, such as accuracy, precision, recall, and AUC-ROC.

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

Explain the key differences between Lasso and Ridge Regression, focusing on their regularization techniques. Highlight how Lasso performs feature selection by shrinking coefficients to zero, while Ridge penalizes large coefficients without eliminating features.

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

Describe the fundamental differences between classification models, which predict categorical outcomes, and regression models, which predict continuous outcomes. Discuss their respective use cases and evaluation metrics.

6. Write a function search_list to check if a target value is in a linked list.

Write a function, search_list, that returns a boolean indicating if the target value is in the linked_list or not. You receive the head of the linked list, which is a dictionary with keys value and next. If the linked list is empty, you’ll receive None.

7. Write a query to find users who placed less than 3 orders or ordered less than $500 worth of product.

Write a query to identify the names of users who placed less than 3 orders or ordered less than $500 worth of product. Use the transactions, users, and products tables.

8. Create a function digit_accumulator to sum every digit in a string representing a floating-point number.

You are given a string that represents some floating-point number. Write a function, digit_accumulator, that returns the sum of every digit in the string.

9. Develop a function to parse the most frequent words used in poems.

You’re hired by a literary newspaper to parse the most frequent words used in poems. Poems are given as a list of strings called sentences. Return a dictionary of the frequency that words are used in the poem, processed as lowercase.

10. Write a function rectangle_overlap to determine if two rectangles overlap.

You are given two rectangles a and b each defined by four ordered pairs denoting their corners on the x, y plane. Write a function rectangle_overlap to determine whether or not they overlap. Return True if so, and False otherwise.

11. How would you design a function to detect anomalies in univariate and bivariate datasets?

If given a univariate dataset, how would you design a function to detect anomalies? What if the data is bivariate?

12. What are the drawbacks of the given student test score data layouts?

Assume you have data on student test scores in two layouts. What are the drawbacks of these layouts? What formatting changes would you make for better analysis? Describe common problems in “messy” datasets.

13. What is the expected churn rate in March for customers who bought subscriptions since January 1st?

You noticed that 10% of customers who bought subscriptions in January 2020 canceled before February 1st. Assuming uniform new customer acquisition and a 20% month-over-month decrease in churn, what is the expected churn rate in March for all customers who bought the product since January 1st?

14. How would you explain a p-value to a non-technical person?

How would you explain what a p-value is to someone who is not technical?

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

What are the Z and t-tests? What are they used for? What is the difference between them? When should you use one over the other?

16. What metrics would you use to determine the value of each marketing channel?

Given data on marketing channels and their costs, identify key metrics to evaluate the value of each channel for a company selling B2B analytics dashboards.

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

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

18. How would you investigate if the redesigned email campaign led to the increase in conversion rates?

Given an increase in new-user-to-customer conversion rates after a redesigned email journey, determine how to investigate if the increase is due to the redesign or other factors.

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 London Stock Exchange Group data analyst interview include:

• Understand Financial Concepts: Familiarize yourself with LSEG’s financial products and services. Having a basic understanding of Capital Markets, Post-Trade Services, and Information Services will be beneficial.
• Excel Proficiency: Brush up on your Excel skills, especially formulas like VLOOKUP and pivot tables. These are crucial for data manipulation and analysis tasks.
• Be Client-Focused: LSEG values client-focused excellence, so highlight experiences showcasing your attention to detail and ability to work under strict deadlines in a client-facing environment.

What is the average salary for a Data Analyst at Lseg (london stock exchange group)?

According to Glassdoor, Data Analysts at LSEG earn between $88K to $138K per year, with an average of $110K per year.

What are the key responsibilities of a Data Analyst at LSEG?

As a Data Analyst at LSEG, you will manage and analyze a wide range of data and create in-depth analyses on assigned research projects. Responsibilities include ensuring data quality, performing quality control, and providing customer feedback. You’ll also become a subject matter expert on capital markets and work on significant technical projects independently.

What skills and qualifications are required for the Data Analyst role at LSEG?

Candidates should have a Bachelor’s degree or equivalent and at least 1 year of relevant experience. Key skills include advanced knowledge of Microsoft Excel, SQL query proficiency, strong analytical and problem-solving abilities, and the capacity to meet strict deadlines. Familiarity with financial datasets and indices is crucial. Excellent communication and attention to detail are also essential.

How does the LSEG ensure a good cultural fit for candidates?

LSEG values a collaborative and dynamic work environment. During the interview process, you’ll have the opportunity to meet various team members to assess mutual compatibility. The company looks for candidates who align with their core values of Integrity, Partnership, Excellence, and Change. This helps ensure that new hires can thrive and contribute positively to the organization’s goals.

What makes LSEG a unique place to work for Data Analysts?

At LSEG, you will be part of a global organization committed to driving financial stability and sustainable growth. The company offers various learning and development opportunities, a diverse and inclusive culture, and comprehensive benefits including healthcare and retirement planning. By joining LSEG, you’ll be contributing to a company that values innovation and sustainability.

To excel in your pursuit of a Data Analyst role at LSEG, align your preparation with their thorough and multifaceted interview process. From aptitude tests assessing logic and problem-solving skills to in-depth technical and behavioral interviews, understanding the nuances of each stage is crucial. Interviewers are focused on gauging your technical proficiency, cultural fit, and enthusiasm toward financial markets and data analytics.

If you want more insights about the company, check out our main LSEG Interview Guide, where we have covered various interview questions that could be asked. At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance needed to ace your LSEG Data Analyst interview.

Good luck with your interview!