Barclays Data Scientist Interview Questions + Guide in 2024

Barclays, established in 1690, is one of the world’s largest and most respected financial institutions, known for its legacy of innovation and success. An important presence in the USA, Barclays offers a wide range of career opportunities with endless possibilities.

As a Data Scientist at Barclays, you will harness mathematics, statistics, and machine learning to support various business functions, including risk management and financial crime prevention. The role involves working closely with multiple teams to develop advanced data analysis models, improve data logistics, and communicate insights effectively to non-technical stakeholders.

This guide will explore the interview process, typical Barclays data scientist interview questions, and tips to help you succeed. Let’s get started!

The interview process usually depends on the role and seniority; however, you can expect the following on a Barclays data scientist interview:

Recruiter/Hiring Manager Call Screening

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

In some cases, the Barclays Data Scientist 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.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the Barclays Data Scientist role usually is conducted through virtual means, including video conference and screen sharing. Questions in this interview stage of about 1 hour may revolve around Barclays’ data systems, SQL queries, statistical analysis concepts like standard deviation, Z-score, and coefficients, as well as machine learning techniques such as feature engineering and model evaluation matrices.

Depending on the seniority of the position, you may also face technical coding questions, commonly focusing on Python (e.g., pandas, mutable vs. immutable objects), and a case study revolving around business problems that require data-driven solutions.

Onsite Interview Rounds

After navigating the technical virtual interview, you’ll be invited to attend the onsite interview loop following a discussion with the recruiter outlining the next stage. During your day at the Barclays office, there will be multiple interview rounds where your technical prowess, programming and ML modeling capabilities will be evaluated against the finalized candidates.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the Data Scientist role at Barclays. Additionally, onsite interviews often seek to understand how you put technical concepts into simple language for non-technical stakeholders and may involve discussing previous projects or specific technical scenarios.

Typically, interviews at Barclays vary by role and team, but common data scientist interviews follow a fairly standardized process across these question topics.

1. Create a function sorting to sort a list of strings in ascending alphabetical order without using the built-in sorted function.

Given a list of strings, write a function, sorting from scratch to sort the list in ascending alphabetical order. Return the new sorted list, rather than modifying the list in-place. Aim for a solution with (O(n \log n)) complexity.

2. 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?

3. What are the drawbacks of the given student test score datasets, and how would you reformat them?

Assume you have data on student test scores in two layouts (dataset 1 and dataset 2). What are the drawbacks of these layouts? What formatting changes would you make for better analysis? Describe common problems in “messy” datasets.

4. 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?

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

Explain what a p-value is in simple terms to someone who is not technical.

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

Describe what Z and t-tests are, their uses, differences, and when to use one over the other.

7. 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 and why it might be preferred over logistic regression in certain scenarios.

8. When would you use a bagging algorithm versus a boosting algorithm?

Compare two machine learning algorithms and provide examples of tradeoffs between using a bagging algorithm and a boosting algorithm.

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

1. Identify the type of model developed by your co-worker for loan approval.
2. Describe how to measure the difference between two credit risk models over a timeframe.
3. List the metrics to track for measuring 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 and impact on model coefficients.

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

Describe the main differences between classification models and regression models, including their objectives and types of output.

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

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

13. 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 and discuss common issues in “messy” datasets.

14. 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 each channel’s value.

15. How would you determine the next partner card for a company?

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

16. How would you investigate the impact of a redesigned email campaign on conversion rates?

Analyze whether an increase in new-user-to-customer conversion rates is due to a redesigned email campaign or other factors. Describe your investigative approach.

You should plan to brush up on any technical skills and prepare comprehensively for the interview process. A few tips for acing your Barclays interview include:

1. Understand Barclays’ Values: Review the Barclays values and mindset by visiting their website. Understand how your experience aligns with their core principles of Respect, Integrity, Service, Excellence, and Stewardship.

2. Be Ready for Business Problem Discussions: Barclays interviews often include case study and business problem discussions. Practice explaining your approach to solving business problems using data science solutions, and be prepared to interact with examples.

3. Demonstrate Your Technical and Communication Skills: Barclays interviews look for strong technical expertise combined with the ability to explain technical concepts to non-technical stakeholders. Prepare to discuss your projects and how you made data-driven decisions and communicated your findings.

What is the average salary for a Data Scientist at Barclays?

What skills are required to work as a Data Scientist at Barclays?

Essential skills include strong data analysis and machine learning capabilities, proficiency in tools like PySpark, SQL, and Python, and experience with data-driven product development. Communication skills to translate technical insights to non-technical stakeholders are also valued.

What is the company culture like at Barclays?

Barclays values respect, integrity, service, excellence, and stewardship. The company fosters a culture where diversity is celebrated, and employees are encouraged to bring their whole selves to work. They emphasize both professional success and personal growth, offering dynamic working options.

Embarking on a career as a Data Scientist at Barclays offers a multifaceted and rewarding experience. Barclays’ commitment to growth and innovation is mirrored in its comprehensive interview process and the opportunities it offers.

If you want to delve deeper into what to expect and how to excel, check out our main Barclays Interview Guide, where we cover a plethora of commonly asked interview questions. We also provide guides for other roles that can help you understand Barclays’ interview process better.

Good luck with your interview!