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Barclays Data Analyst Interview Questions

Company: Barclays
Designation: Data Analyst
Year of Experience Required: 0 to 4 years
Technical Expertise: SQL, Python/R, Statistics, Machine Learning, Case Studies
Salary Range: 12LPA – 30LPA

Barclays plc, headquartered in London, England, is a British multinational investment bank and financial services company. With operations in personal banking, corporate banking, wealth management, and investment management, Barclays is a global leader in the financial sector. If you’re preparing for a Data Analyst role at Barclays, here’s a detailed breakdown of their interview process and the types of questions you can expect.

Barclays Data Analyst Interview Questions

If you are searching job opportunities for the role of Data Scientist, make sure you are able to solve these questions in time limit.

Interview Process

The Barclays Data Analyst interview process typically consists of 5 rounds, each designed to evaluate different aspects of your technical and analytical skills:

Round 1 – Telephonic Screening

Focus: Basic understanding of Data Science concepts, SQL, and Python/R.
Format: You’ll be asked to explain your projects and solve a few coding or SQL problems.

Round 2 – Walk-in/Face-to-Face Technical Round

Focus: Advanced SQL, coding, and problem-solving.
Format: You’ll solve problems on a whiteboard or shared document.

Round 3 – Project Analysis

Focus: Deep dive into your past projects.
Format: You’ll be asked to explain your approach, tools used, and the impact of your work.

Round 4 – Case Studies

Focus: Business problem-solving and data-driven decision-making.
Format: You’ll be given a real-world scenario and asked to propose solutions.

Round 5 – Hiring Manager Round

Focus: Cultural fit, communication skills, and long-term career goals.
Format: Behavioral questions and high-level discussions about your experience.

Difficulty of Questions

SQL – 8/10

1) How can you calculate the average order value (AOV) for each customer?

SELECT customer_id, AVG(total_amount) AS avg_order_value
FROM orders
GROUP BY customer_id;

2) How can you retrieve the highest-paid employee from each department?

SELECT e.name, e.department_id, e.salary
FROM employees e
WHERE e.salary = (SELECT MAX(salary) FROM employees WHERE department_id = e.department_id);

3) How do you find orders where the total purchase amount is greater than $500?

SELECT order_id, SUM(price * quantity) AS total_amount
FROM order_details
GROUP BY order_id
HAVING total_amount > 500;

4) How can you find customers who have never placed an order?

SELECT c.customer_id, c.name
FROM customers c
LEFT JOIN orders o ON c.customer_id = o.customer_id
WHERE o.order_id IS NULL;

5) How can you get the number of orders placed per month?

SELECT DATE_FORMAT(order_date, '%Y-%m') AS order_month, COUNT(*) AS total_orders
FROM orders
GROUP BY order_month
ORDER BY order_month DESC;

R/Python – 7/10

1) Use a generator-based context manager to create a context that prints “Entering context” when entering and “Exiting context” when exiting.

2) Given a Pandas DataFrame with columns “Category” and “Value”, group the data by “Category” and calculate the sum of “Value” for each group.

3) Create a decorator that checks if the arguments passed to a function are of the expected types.

4) Given a tree represented by nested lists, implement a DFS function to perform a pre-order traversal and print the nodes.

5) Given a list of tuples, where each tuple contains a key and a value, use collections.defaultdict to group the values by their keys.

Statistics/ML

1) What is one way that you would handle an imbalanced data set that’s being used for prediction (i.e., vastly more negative classes than positive classes)?

One effective way to handle an imbalanced dataset is oversampling the minority class. This means artificially increasing the number of positive class samples by duplicating existing data points or generating synthetic data using techniques like SMOTE (Synthetic Minority Over-sampling Technique).For example, if a dataset has 90% negative and 10% positive cases, oversampling can balance the classes, helping the model learn patterns better and improving predictive accuracy. Other approaches include undersampling the majority class, using cost-sensitive learning, or leveraging ensemble methods like boosting.

2) What risks and pitfalls can compromise your data during transmission and loading?

Data can be compromised due to:

• Network Interception: Unencrypted data can be intercepted during transmission.
• Data Corruption: Errors during transmission or loading can lead to missing or altered data.
• Duplicate or Incomplete Data: Poor handling can result in data loss or duplication.
• Schema Mismatch: If data formats or structures don’t align, loading may fail or introduce inconsistencies.
• Unauthorized Access: Weak security measures can expose data to unauthorized users.

To mitigate these risks, use encryption, validation checks, logging, and proper access controls.

3) You have two models of comparable accuracy and computational performance. Which one should you choose for production and why?

If both models have similar accuracy and performance, choose the one that is:

• More Interpretable: A model that is easier to explain and debug is preferable.
• More Robust: A model that generalizes better across different datasets is ideal.
• Less Complex: Simpler models are easier to maintain, deploy, and scale.

For example, if a Decision Tree and a Neural Network have similar accuracy, the Decision Tree is often preferred due to better interpretability.

4) When modifying an algorithm, how do you know that your changes are an improvement over not doing anything?

To measure improvement:

• Compare Performance Metrics: Check accuracy, precision, recall, F1-score, or other relevant metrics before and after modifications.
• Use Cross-Validation: Ensure changes improve results across different data splits.
• Conduct A/B Testing: Deploy old and new versions to see if the new one performs better in real-world conditions.
• Monitor Generalization: Ensure the model improves on unseen data and avoids overfitting.

If all indicators show an improvement, the changes are beneficial.

5) What happens when we add a variable and it increases R-Squared but decreases Adjusted R-Squared?

When adding a new variable:

• R-Squared always increases or remains the same because it measures how well the model explains the variance in the data.
• Adjusted R-Squared may decrease if the new variable does not contribute significantly to the model.

This happens because Adjusted R-Squared penalizes unnecessary variables to prevent overfitting. If a new variable adds noise instead of meaningful information, Adjusted R-Squared drops.

To ensure meaningful additions, check the p-value and multicollinearity before adding new variables.

Case Study

Problem Statement:

Barclays wants to enhance its fraud detection system to minimize financial losses from fraudulent transactions while reducing false positives (legitimate transactions mistakenly flagged as fraud). Your task as a Data Analyst is to analyze transaction data, identify fraudulent patterns, and recommend an improved fraud detection strategy.

Dataset Overview:

You have access to a dataset containing credit and debit card transactions from Barclays customers. The dataset includes:

• Transaction_ID – Unique identifier for each transaction
• Customer_ID – Unique identifier for each customer
• Transaction_Amount – Amount spent in a transaction
• Merchant_Category – Category of the merchant (e.g., grocery, electronics, travel)
• Transaction_Location – Location where the transaction occurred
• Device_Used – Type of device used for the transaction (e.g., mobile, laptop, ATM)
• Transaction_Time – Timestamp of the transaction
• Fraud_Flag – 1 if the transaction is fraudulent, 0 if not
• Previous_Fraud_History – Indicator of whether the customer has been involved in past fraudulent transactions
• Customer_Spending_Pattern – Normal spending behavior of the customer

Key Questions to Answer:

1. What patterns indicate fraudulent transactions?

• Are fraudsters targeting specific merchant categories (e.g., luxury goods, online transactions)?
• Do fraudulent transactions have unusual spending amounts compared to the customer’s normal behavior?
• Are fraud transactions occurring from new locations or unknown devices?

2. How can Barclays improve its fraud detection system?

• Can machine learning models be used to improve fraud detection accuracy?
• How can Barclays balance fraud prevention with customer experience to avoid false positives?
• Should Barclays introduce real-time transaction verification for high-risk transactions?

3. What strategies can Barclays implement to minimize fraud risk?

• How can Barclays use customer spending behavior profiling to detect anomalies?
• Should Barclays implement two-factor authentication for high-risk transactions?
• Can Barclays collaborate with merchant networks to identify suspicious transactions early?

Key Insights & Business Recommendations

1. Identifying Fraudulent Patterns

• Unusual Spending Amounts: Transactions that significantly deviate from a customer’s normal spending habits are often fraudulent.
• Transaction from New Locations: If a customer primarily transacts in the UK but suddenly makes multiple transactions from another country, it could indicate fraud.
• Multiple Small Transactions in a Short Time: Fraudsters often test stolen cards by making small purchases before making large transactions.
• Use of New Devices: If a transaction occurs from a new device not previously used by the customer, it should raise suspicion.

2. Enhancing Fraud Detection Mechanisms

• AI-Powered Fraud Detection: Implementing machine learning models that continuously learn from transaction data can help identify fraudulent patterns with high accuracy.
• Dynamic Transaction Limits: Barclays can apply adaptive spending limits for customers based on their transaction history to prevent excessive unauthorized transactions.
• Geolocation & Behavioral Analytics: Using real-time location data and behavioral insights can help identify fraud before a transaction is processed.

3. Fraud Prevention Strategies

• Real-Time Alerts & Customer Verification: Barclays should implement instant alerts for suspicious transactions, allowing customers to verify or decline them in real time.
• Multi-Factor Authentication for High-Risk Transactions: Implementing OTP (one-time passwords) or biometric authentication for large or unusual transactions can reduce fraud.
• Collaborating with Law Enforcement & Merchant Networks: Barclays can work with financial crime agencies and merchant networks to share fraud intelligence and prevent large-scale financial fraud.

Basic, you can practice a lot of case studies and other statistics topics here –
https://thedatamonk.com/data-science-resources/

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