Swiss Re Data Analyst Interview Questions + Guide in 2024

Swiss Re is a global leader in reinsurance, insurance, and insurance-based risk transfer services. With expertise in Property and casualty and Life and health insurance, Swiss Re is known for its innovative approaches to managing risks such as natural disasters and cybercrime. Employing over 14,000 professionals worldwide, Swiss Re fosters a collaborative and inclusive culture that encourages innovative thinking.

As a Data Analyst at Swiss Re, you will work as part of a cross-functional team to transform raw data into actionable insights. Your responsibilities will include client data integration, driving strategic decision-making, and optimizing business processes. The role requires proficiency in advanced tools and methodologies to enhance analytics capabilities and develop key performance metrics.

This guide will take you through the interview process, offering insights into typical Swiss Re data analyst interview questions, the stages involved, and essential preparation tips to help you succeed.

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

Recruiter/Hiring Manager Call Screening

If your CV is among the shortlisted few, a recruiter from the Swiss Re 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 Swiss Re 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.

Technical Virtual Interview

Successfully navigating the recruiter round will invite you to the technical screening round. Technical screening for the Swiss Re 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 Swiss Re’s data systems, ETL pipelines, and SQL queries.

In the case of data analyst roles, take-home assignments regarding product metrics, analytics, and data visualization are incorporated. In addition, your proficiency in hypothesis testing, probability distributions, and machine learning fundamentals may also be assessed during the round.

Case studies and similar real-scenario problems may also be assigned depending on the position’s seniority.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds, varying with the role, will be conducted during your day at the Swiss Re office. Your technical prowess, including programming and ML modeling capabilities, will be evaluated against the 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 Swiss Re.

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

1. Write a SQL query to select the 2nd highest salary in the engineering department.

Write an SQL query to select the second-highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

2. 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.

3. 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 word that reads the same forwards and backward (e.g., ‘reviver,’ ‘madam,’ ‘deified,’ ‘civic’).

4. Write a query to find users currently “Excited” and never “Bored” with a campaign.

Write a query to find all users currently “Excited” and have never been “Bored” with a campaign.

5. Write a function moving_window to find the moving window average of a list of numbers.

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

6. What are type I and type II errors in hypothesis testing?

In hypothesis testing, type I errors (false positives) occur when you reject a true null hypothesis. In contrast, type II errors (false negatives) occur when you fail to reject a false null hypothesis. Describe the probability of making each type of error mathematically.

7. How would you select Dashers for Doordash deliveries in NYC and Charlotte?

Doordash is launching delivery services in New York City and Charlotte. How would you decide which Dashers to select for these deliveries? Would the selection criteria be the same for both cities?

8. How would you improve Google Maps and measure success?

As a PM on Google Maps, how would you improve the app? What metrics would you check to see if your feature improvements are successful?

9. Why are job applications decreasing despite stable job postings?

On a job board, the number of job postings per day has remained stable, but the number of applicants has decreased. Why might this be happening?

10. How would you analyze the performance of a new LinkedIn feature without A/B testing?

As a data scientist at LinkedIn, you need to analyze a new feature that allows candidates to message hiring managers directly during the interview process. Due to engineering constraints, the company can’t A/B test the feature before launching it. How would you analyze its performance?

11. 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?

12. 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?

13. 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 creating such a model and explain the predictions to non-technical stakeholders?

14. 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 whether a borrower will repay a personal loan. How would you evaluate whether a decision tree algorithm is the correct model for the problem? How would you assess the performance of the model before and after deployment?

15. 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.

16. What’s the probability that the second card is not an Ace?

You must draw two cards from a shuffled deck, one at a time. Calculate the probability that the second card drawn is not an Ace.

17. How much do you expect to pay 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 must purchase a box office ticket for $70. Calculate the expected cost and the amount of money you should set aside for the game.

18. Is a coin fair if it lands tails 8 out of 10 times?

You flip a coin 10 times, and it comes up tails 8 times and heads twice. Based on this outcome, determine if the coin is fair.

19. What is the difference between covariance and correlation?

Explain the difference between covariance and correlation, and provide an example to illustrate the concepts.

Here are some tips to help you prepare effectively for your interview as a Swiss Re data analyst:

1. Brush Up on Your Skills: Plan to brush up on technical skills and try as many practice interview questions and mock interviews as possible on Interview Query.

2. Have a Problem-Solving Mindset: Demonstrate your problem-solving skills. Swiss Re often evaluates thought processes and problem-solving abilities more than getting the exact answer.

3. Understand the Company’s Data Needs: Swiss Re aims to leverage data for advanced analytics, so be prepared to discuss your experience with ETL pipelines, data warehousing, and data integration.

What is the average salary for a Data Analyst at Swiss Re?

According to Glassdoor, data analysts at Swiss Re earn between $62K to $67K per year with an average of $64K per year.

What skills and qualifications are essential for the Data Analyst role at Swiss Re?

The Data Analyst role at Swiss Re requires a Bachelor of Science degree and at least 5 years of relevant experience. Key technical skills include proficiency in SQL and Python or R and a solid understanding of data structures, ETL processes, and data integration techniques. Additionally, presenting complex data and insights clearly and concisely is crucial for driving informed decision-making. Strong communication skills are essential for effectively collaborating with cross-functional teams and stakeholders.

What’s the company culture like at Swiss Re?

Swiss Re fosters an inclusive and flexible work environment where innovation and diverse perspectives are highly encouraged. The company values equal opportunities, sustainability, and professional development, making it a place to bring your authentic self to work truly.

Swiss Re offers an exhilarating opportunity for data analysts to be at the forefront of transforming raw data into actionable insights crucial for driving their business success.

For those aspiring to join Swiss Re, preparation is key. For more insights about the company, check out our main Swiss Re Interview Guide, where you can learn more about Swiss Re’s interview process for different positions and other interview questions that could be asked.

Good luck with your interview!