Chubb is a leading global insurance firm with operations in 54 countries, offering a comprehensive range of insurance products and services. Distinguished by its robust financial strength and exceptional service, Chubb is committed to providing innovative solutions to its clients.

If you're passionate about data infrastructure, metrics, and coding, the Data Analyst position at Chubb could be the perfect fit for you. This role requires strong technical and analytical skills and involves working closely with key stakeholders to deliver data-driven solutions. The interview process usually consists of initial screening, technical interviews, an adaptability interview focusing on insurance and agile methodologies, and an HR round. Prepare for questions on SQL, Python, machine learning, and general programming.

Explore this opportunity with Interview Query for insights and guidance through the interview process at Chubb.

Submitting Your Application

The first step in the interview process for a Data Analyst position at Chubb is to submit a compelling application that reflects your technical skills and interest in joining the company. Make sure to carefully review the job description and tailor your CV and cover letter according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, highlight relevant skills and mention your work experiences, especially those that pertain to data analysis, SQL, Python, machine learning, and previous roles in similar industries like insurance.

Initial Screening Round

If your CV makes it past the initial review, a recruiter from Chubb will contact you for an initial screening call. This call often verifies key details about your experiences and skill level. During this phase, expect behavioral questions to gauge your alignment with the company’s values and culture.

In some cases, you may also speak with the hiring manager during this call. They are likely to provide more insights into the role and the company and may touch upon some surface-level technical and behavioral questions.

The recruiter call typically lasts about 30 minutes.

Technical Interview

The next step involves a technical interview, which may be conducted virtually. This round lasts approximately 45 minutes and delves deep into your technical skills. Be prepared to:

• Explain your data analytics projects
• Answer SQL questions (e.g., determine the second-highest salary, compare RANK vs DENSE_RANK)
• Answer Python questions (e.g., global scope, swapping two numbers without a third variable)
• Discuss your favorite machine learning algorithms like SVM or logistic regression
• Share your experience with data imputation, data analysis processes, and specific libraries you've used

This round will also include questions that assess general programming knowledge and your problem-solving abilities in technical scenarios.

Adaptability Interview

Following the technical interview, you will undergo an adaptability interview focusing on industry-specific knowledge (e.g., insurance) and methodologies (e.g., agile). This round assesses your flexibility and ability to adapt to the company's work environment and methodologies.

HR Round

The final stage involves an HR round where past experiences, cultural fit, and salary expectations are discussed. This interaction is also an excellent opportunity for you to ask any pending questions about the role, the team, or the company.

Quick Tips for Chubb Data Analyst Interviews

• Brush Up on SQL and Python: Be prepared for basic to intermediate questions on SQL and Python, as these are the core technical skills required for the role.
• Understand Chubb’s Business: Familiarize yourself with Chubb's core services and market. Knowing some details about their data infrastructure, metrics, and analytics approaches can help you make a strong impression.
• Behavioral and Adaptability Testing: Be ready to discuss how you handle data projects, problem-solving in real-world scenarios, and adapt to rapidly changing environments, especially within the insurance industry context.

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

• Write a SQL query to select the 2nd highest salary in the engineering department. Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

• Write a function get_ngrams to return a dictionary of n-grams and their frequency in a string. Write a function get_ngrams to take in a word (string) and return a dictionary of n-grams and their frequency in the given string.

• Write a function to determine if a string is a palindrome. Given a string, write a function to determine if it is a palindrome. A palindrome reads the same forwards and backwards.

• Write a query to find users currently "Excited" and never "Bored" with a campaign. Write a query to find all users that are currently "Excited" and have never been "Bored" with a campaign.

• Write a function moving_window to find the moving window average of a list. Given a list of numbers nums and an integer window_size, write a function moving_window to find the moving window average.

• What methods could you use to increase recall in product search results without changing the search algorithm? As a data scientist at Amazon, you want to improve the search results for product searches but cannot change the underlying logic in the search algorithm. What methods could you use to increase recall?

• What metrics would you use to track the accuracy and validity of a spam classifier model? You are tasked with building a spam classifier for emails and have built a V1 of the model. What metrics would you use to track the accuracy and validity of the model?

• How would you justify the complexity of a neural network model and explain its predictions to non-technical stakeholders? Your manager asks you to build a model with a neural network to solve a business problem. How would you justify the complexity of building such a model and explain the predictions to non-technical stakeholders?

• How would you evaluate and validate a decision tree model for predicting loan repayment? As a data scientist at a bank, you are tasked with building a decision tree model to predict if a borrower will pay back a personal loan. How would you evaluate whether using a decision tree algorithm is the correct model for the problem? How would you evaluate the performance of the model before deployment and after?

• When would you use a bagging algorithm versus a boosting algorithm? You are comparing two machine learning algorithms. In which case would you use a bagging algorithm versus a boosting algorithm? Provide an example of the tradeoffs between the two.

• What's the probability that the second card drawn from a shuffled deck is not an Ace? You have to draw two cards from a shuffled deck, one at a time. Calculate the probability that the second card is not an Ace.

• What are type I and type II errors in hypothesis testing? Explain the difference between type I errors (false positives) and type II errors (false negatives) in hypothesis testing. Bonus: Describe the probability of making each type of error mathematically.

• How much do you expect to pay and set aside for a sports game ticket? You can buy a scalped ticket for $50 with a 20% chance of not working. If it doesn't work, you'll need to buy a box office ticket for $70. Calculate the expected cost and the amount of money you should set aside for the game.

• Is a coin that lands tails 8 out of 10 times fair? You flip a coin 10 times, and it comes up tails 8 times and heads twice. Determine if the coin is fair.

• What is the difference between covariance and correlation? Explain the difference between covariance and correlation, and provide an example.

• What are type I and type II errors in hypothesis testing? Explain the difference between type I errors (false positives) and type II errors (false negatives) in hypothesis testing. Optionally, describe the probability of making each type of error mathematically.

• How would you select Dashers for Doordash deliveries in NYC and Charlotte? Doordash is launching delivery services in New York City and Charlotte. Describe the process for selecting Dashers (delivery drivers) and discuss whether the criteria for selection should be the same for both cities.

• How would you improve Google Maps and measure success? As a PM on Google Maps, suggest improvements for the app. Specify the metrics you would use to evaluate the success of these feature improvements.

• Why are job applications decreasing despite steady job postings? You observe that the number of job postings per day on a job board has remained constant, but the number of applicants has been decreasing. Analyze potential reasons for this trend.

• How would you analyze the performance of a new LinkedIn feature without A/B testing? As a data scientist at LinkedIn, you need to evaluate a new feature that allows candidates to message hiring managers directly during the interview process. Due to engineering constraints, A/B testing is not possible. Describe how you would analyze the feature's performance.

What is the average salary for a Data Analyst at Chubb?

Q: What does the interview process for a Data Analyst position at Chubb involve? The interview process typically includes at least four rounds: an initial screening round, a technical interview, an adaptability interview focusing on insurance and agile methodologies, and a final HR interview. Expect questions on SQL, Python, machine learning, and general programming.

Q: What kind of technical questions can I expect during the interview? You can expect questions related to SQL and Python, such as determining the second highest salary, differences between merge and join, global scope in Python, data imputation techniques, and questions about your favorite ML algorithms like SVM and logistic regression.

Q: What skills are crucial for the Data Analyst role at Chubb? Chubb looks for data analysts with strong technical expertise in SQL, Python, and data architecture. Additionally, familiarity with data modeling, data analysis, Microsoft Excel, and tools like JIRA and Confluence are essential. Strong analytical, problem-solving, and communication skills are also important.

Q: What is the company culture like at Chubb? Chubb places a strong emphasis on its core values of integrity, client focus, respect, excellence, and teamwork. They are committed to maintaining a diverse and inclusive environment where employees can thrive and be recognized for their contributions.

Q: How can I prepare for an interview at Chubb? To prepare, research Chubb's business and values, practice common interview questions, particularly those related to SQL, Python, and data analysis, and review your past projects and experiences. Utilize platforms like Interview Query to practice technical questions and interview scenarios.

Conclusion

The Data Analyst position at Chubb offers a dynamic and engaging opportunity to work on significant data-driven projects that shape the company's business strategies and operations. With a clear emphasis on technical expertise and collaborative problem-solving, Chubb seeks individuals who are passionate about data and its potential to drive innovation within the insurance sector.

The interview process at Chubb is comprehensive, typically involving multiple rounds that assess both technical abilities in SQL, Python, and machine learning, as well as adaptability and problem-solving skills. Candidates can expect a mix of technical, managerial, and HR rounds, providing a thorough evaluation of your fit for the role.

If you want more insights about the company, check out our main Chubb Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about Chubb's interview process for different positions.

At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every Chubb data analyst interview question and challenge.

You can check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!