Verisk Analytics Data Analyst Interview Questions + Guide in 2024

Verisk Analytics is a leader in data analytics and technology, empowering communities and businesses globally to make informed decisions on risk. Known for its dedication to innovation and ethical practices, Verisk supports diverse industries by delivering comprehensive insights powered by big data.

Ready to join Verisk? Our Interview Query guide will walk you through the interview process, provide sample Verisk Analytics data analyst interview questions and help you prepare effectively. Let’s get started!

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

Recruiter/Hiring Manager Call Screening

If your CV is among the shortlisted few, a recruiter from Verisk Analytics 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 hiring manager joins the screening round to answer your queries about the role and the company itself. They may also engage 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. The technical screening for the data analyst role at Verisk is typically conducted virtually, including video conference and screen sharing. In this 1-hour interview stage, questions may revolve around Verisk’s data systems, ETL pipelines, and SQL queries.

Take-home assignments regarding product metrics, analytics, and data visualization are incorporated for data analyst roles. 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

After a second recruiter call outlining the next stage, you’ll be invited to attend the on-site interview loop. During your day at the Verisk office, multiple interview rounds, varying with the role, will be conducted. 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 Verisk.

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

1. Create a function combinational_dice_rolls to dump all possible combinations of dice rolls.

Given n dice each with m faces, write a function combinational_dice_rolls to dump all possible combinations of dice rolls. Bonus: Can you do it recursively?

2. Develop a function is_subsequence to find out if one string is a subsequence of another.

Given two strings, string1 and string2, write a function is_subsequence to find out if string1 is a subsequence of string2.

3. Write a function to return a list of all prime numbers up to a given integer N.

Given an integer N, write a function that returns a list of all of the prime numbers up to N. Return an empty list if there are no prime numbers less than or equal to N.

4. Create a function to add the frequency of each character in a string after each character.

Given a string sentence, return the same string with an addendum after each character of the number of occurrences a character appeared in the sentence. Do not treat spaces as characters and exclude characters in the discard_list.

5. Write a function sorting to sort a list of strings in ascending alphabetical order from scratch.

Given a list of strings, write a function sorting to sort the list in ascending alphabetical order without using the built-in sorted function. Return the new sorted list rather than modifying the list in-place.

6. What factors could have biased Jetco’s boarding time study results?

Jetco’s study showed the fastest average boarding times among airlines. Identify potential biases in the study and what specific aspects you would investigate to validate the results.

7. How would you debug the marriage attribute issue in auto insurance data?

In the auto insurance data, the marriage attribute is marked TRUE for all customers. Describe the steps you would take to debug this issue, including what data to examine and how to determine the actual marital status of the clients.

8. How would you evaluate the suitability and performance of a decision tree model for predicting loan repayment?

You are tasked with building a decision tree model to predict if a borrower will repay a personal loan. How would you evaluate whether a decision tree is the correct model for this problem? If you proceed with the decision tree, how would you evaluate its performance before and after deployment?

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

Explain the primary differences between classification models and regression models in machine learning.

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

Compare two machine learning algorithms. In which scenarios would you use a bagging algorithm versus a boosting algorithm? Provide examples of the tradeoffs between the two.

11. How would you determine if you have enough data to build an accurate ETA prediction model?

You have 1 million app rider journey trips in Seattle and want to build a model to predict ETA after a ride request. How would you know if you have sufficient data to create an accurate model?

12. How would you build a model to predict which merchants DoorDash should acquire in a new market?

As a data scientist at DoorDash, how would you build a model to predict which merchants the company should target for acquisition when entering a new market?

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

Explain the concept of a p-value in simple terms to someone without a technical background.

14. What is the probability that a red marble was pulled from Bucket #1?

Given two buckets with different distributions of red and black marbles, calculate the probability that a red marble was pulled from Bucket #1.

15. What is the probability that Amy wins the game by rolling a six first?

Amy and Brad take turns rolling a fair six-sided die, with Amy starting first. Calculate the probability that Amy wins by rolling a six before Brad.

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 Verisk Analytics data analyst interview include:

• Be Detail-Oriented: Verisk values accuracy and thoroughness, especially since you’ll be handling vast amounts of complex data. Ensure you showcase your attention to detail in every task.
• Understand the Business Context: Highlight how your data analysis can drive business decisions and contribute to Verisk’s objectives by providing actionable insights.
• Showcase Collaboration: Verisk’s work environment is collaborative, so demonstrate your ability to work well with cross-functional teams and communicate your findings effectively.

What is the average salary for a Data Analyst at Verisk Analytics?

According to Glassdoor, Data Analyst at Verisk Analytics earn between $79K to $108K per year, with an average of $92K per year.

What technical skills are required for the Data Analyst role at Verisk Analytics?

Verisk seeks candidates with proficiency in data analysis tools such as Excel, SQL, and Python. Experience with data visualization tools like Tableau, Power BI, or Looker is also important. A strong understanding of statistical methods, database management, and AI technologies, like Large Language Models, is highly valued.

Can you describe the job responsibilities of a Data Analyst at Verisk Analytics?

A Data Analyst at Verisk Analytics is responsible for analyzing complex datasets to identify trends, creating and maintaining reports and dashboards, ensuring data quality, performing ad hoc analyses, and collaborating with cross-functional teams to deliver actionable insights.

What experience and educational background are preferred for a Data Analyst at Verisk Analytics?

Candidates should have at least a Bachelor’s degree in a related field such as Statistics, Mathematics, Computer Science, or Business, along with 3+ years of relevant experience. A strong background in P&C insurance data analysis and experience with SQL, Excel, and BI tools are advantageous.

What makes Verisk Analytics a great place to work?

Verisk Analytics has been recognized as a Great Place to Work for its outstanding workplace culture, valuing inclusivity, diversity, learning, and innovation. Employees enjoy work flexibility, support, coaching, and numerous opportunities for personal and professional growth.

The interview process for a Data Analyst position at Verisk Analytics offers a comprehensive and thorough assessment of both your technical skills and cultural fit. With multiple stages, including initial interviews, rigorous tests, and detailed discussions, Verisk ensures they are onboarding the best talent to drive their mission of empowering communities and businesses to make better decisions through innovative data analytics.

If you want more insights about the company, check out our main Verisk Analytics 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 Verisk Analytics’ interview process for different positions.

Good luck with your interview!