Verisk Analytics Data Analyst Interview Questions + Guide in 2025

Verisk Analytics is a leading data analytics and technology partner that empowers businesses and communities to make informed decisions on risk through its innovative solutions.

As a Data Analyst at Verisk Analytics, you will be at the forefront of transforming data into actionable insights that support business objectives. Your key responsibilities will include analyzing complex datasets to identify trends, patterns, and insights that drive strategic decision-making. You will collaborate closely with cross-functional teams, ensuring data accuracy and providing ongoing reporting to stakeholders. A strong emphasis on data normalization, quality assurance, and effective communication will be vital in your role, as you will be expected to convey your findings clearly to both technical and non-technical audiences.

The ideal candidate will possess a detail-oriented mindset, proficient technical skills in SQL, Excel, and data visualization tools, and a collaborative spirit. A background in statistical analysis and experience in the insurance industry are also advantageous. At Verisk, we value continuous improvement, and you should be ready to identify process enhancements and share innovative ideas with your team.

This guide will help you prepare for your interview by highlighting the crucial skills and experiences that Verisk Analytics seeks in a Data Analyst, equipping you with the knowledge to articulate your fit for the role effectively.

In this section, we’ll review the various interview questions that might be asked during a Data Analyst interview at Verisk Analytics. The interview process will likely assess your technical skills, analytical thinking, and ability to communicate effectively within a collaborative environment. Be prepared to demonstrate your knowledge of data analysis tools, your understanding of data quality, and your problem-solving abilities.

Technical Skills

1. Can you explain the process you follow for data cleaning and normalization?

Understanding data cleaning and normalization is crucial for a Data Analyst role, as it ensures the integrity and usability of data.

How to Answer

Discuss your systematic approach to identifying and correcting errors in datasets, including the tools and techniques you use for normalization.

Example

“I typically start by assessing the data for inconsistencies and missing values. I use tools like SQL for querying and Python for data manipulation. For normalization, I ensure that all data is in a consistent format, using techniques like regular expressions to clean up any poorly formatted entries.”

2. Describe your experience with SQL and how you have used it in your previous roles.

SQL is a fundamental skill for data analysts, and your ability to use it effectively will be evaluated.

How to Answer

Highlight specific projects where you utilized SQL to extract, manipulate, or analyze data, and mention any complex queries you’ve written.

Example

“In my last role, I used SQL extensively to extract data from our relational database. I wrote complex queries involving joins and subqueries to generate reports that informed our marketing strategies. This helped the team identify key customer segments and improve our targeting efforts.”

3. How do you ensure data accuracy and quality in your analyses?

Data quality is paramount in analytics, and interviewers will want to know your methods for maintaining it.

How to Answer

Discuss the checks and balances you implement to verify data accuracy, including any tools or methodologies you use.

Example

“I implement a multi-step validation process where I cross-reference data against known benchmarks and perform consistency checks. Additionally, I use automated scripts to flag anomalies, ensuring that any discrepancies are addressed before analysis.”

4. Can you provide an example of a complex dataset you analyzed and the insights you derived from it?

This question assesses your analytical skills and ability to derive actionable insights from data.

How to Answer

Choose a specific project where your analysis led to significant findings or decisions, detailing the methods you used.

Example

“I worked on a project analyzing customer purchase behavior over a year. By segmenting the data and applying regression analysis, I identified trends that indicated a shift in customer preferences. This insight led to a successful product repositioning strategy that increased sales by 15%.”

5. What data visualization tools are you familiar with, and how have you used them?

Data visualization is key for communicating insights, and familiarity with tools is essential.

How to Answer

Mention specific tools you’ve used and describe how you’ve applied them to present data effectively.

Example

“I have experience with Tableau and Power BI for data visualization. In my previous role, I created interactive dashboards that allowed stakeholders to explore data trends in real-time, which facilitated data-driven decision-making across departments.”

Problem-Solving and Analytical Thinking

1. Describe a time when you encountered a significant data issue. How did you resolve it?

This question evaluates your problem-solving skills and your ability to handle challenges.

How to Answer

Provide a specific example, detailing the issue, your approach to resolving it, and the outcome.

Example

“Once, I discovered that a significant portion of our sales data was missing due to a system error. I collaborated with the IT team to identify the root cause and implemented a new data entry protocol to prevent future occurrences. This proactive approach not only resolved the immediate issue but also improved our data integrity moving forward.”

2. How do you prioritize your tasks when working on multiple projects?

Time management is crucial in a fast-paced environment, and interviewers will want to know your strategy.

How to Answer

Discuss your approach to prioritization, including any tools or methods you use to manage your workload.

Example

“I use a combination of project management tools like Trello and a priority matrix to assess the urgency and importance of tasks. This helps me focus on high-impact projects while ensuring that deadlines are met across the board.”

3. Can you explain a statistical method you frequently use in your analyses?

Statistical knowledge is important for a Data Analyst, and interviewers will want to gauge your familiarity with various methods.

How to Answer

Choose a statistical method relevant to the role and explain how you apply it in your work.

Example

“I frequently use regression analysis to understand relationships between variables. For instance, I applied it to analyze the impact of marketing spend on sales revenue, which helped the team allocate resources more effectively.”

4. How do you approach continuous improvement in your data analysis processes?

This question assesses your commitment to optimizing workflows and processes.

How to Answer

Discuss specific strategies you employ to enhance your analysis processes and improve efficiency.

Example

“I regularly seek feedback from team members on my reports and analyses. I also stay updated on industry best practices and tools, which allows me to implement new techniques that streamline our processes and improve the quality of our outputs.”

5. What steps do you take to communicate complex data findings to non-technical stakeholders?

Effective communication is key in a collaborative environment, and your ability to convey complex information will be evaluated.

How to Answer

Explain your approach to simplifying complex data insights for a non-technical audience.

Example

“I focus on using clear visuals and straightforward language when presenting data findings. I often create summary reports that highlight key insights and actionable recommendations, ensuring that stakeholders can easily grasp the implications of the data.”

Here are some tips to help you excel in your interview.

Understand the Interview Structure

The interview process at Verisk Analytics typically consists of multiple stages, including a phone screening, technical assessments, and discussions with management. Familiarize yourself with this structure so you can prepare accordingly. Expect to demonstrate your analytical skills through a practical test, which may involve open-ended questions that assess your problem-solving abilities. Being aware of this format will help you manage your time and responses effectively.

Showcase Your Technical Proficiency

As a Data Analyst, you will be expected to have a solid understanding of SQL, Excel, and potentially Python. Brush up on these skills and be prepared to discuss your experience with them in detail. You may be asked to solve problems or analyze data during the interview, so practice common data manipulation tasks and be ready to explain your thought process. Highlight any experience you have with data normalization, quality assurance, and using regular expressions, as these are relevant to the role.

Emphasize Collaboration and Communication

Verisk values teamwork and effective communication. Be prepared to discuss how you have successfully collaborated with others in past roles. Share specific examples of how you communicated complex data findings to both technical and non-technical stakeholders. This will demonstrate your ability to work within a diverse team and your commitment to fostering a collaborative work environment.

Prepare for Behavioral Questions

Expect behavioral questions that assess your problem-solving skills, attention to detail, and ability to handle challenges. Use the STAR (Situation, Task, Action, Result) method to structure your responses. For instance, you might be asked to describe a time when you identified a data quality issue and how you resolved it. This approach will help you provide clear and concise answers that highlight your competencies.

Align with Company Culture

Verisk Analytics prides itself on a culture of innovation, inclusivity, and continuous improvement. Research the company’s values and think about how your personal values align with theirs. Be ready to discuss how you can contribute to their mission of helping businesses and society become more resilient and sustainable. Showing that you understand and resonate with their culture will set you apart as a candidate.

Ask Insightful Questions

Prepare thoughtful questions to ask your interviewers. This not only shows your interest in the role but also gives you a chance to assess if Verisk is the right fit for you. Consider asking about the team dynamics, opportunities for professional development, or how the company measures success in the Data Analyst role. Engaging in this dialogue can leave a positive impression and demonstrate your proactive nature.

Practice Mindfulness and Composure

Interviews can be nerve-wracking, but maintaining a calm and composed demeanor is crucial. Practice mindfulness techniques to help manage anxiety and stay focused. Remember that the interview is as much about you assessing the company as it is about them evaluating you. Approach the conversation with confidence and authenticity.

By following these tips, you will be well-prepared to showcase your skills and fit for the Data Analyst role at Verisk Analytics. Good luck!

The interview process for a Data Analyst position at Verisk Analytics is structured to assess both technical skills and cultural fit within the organization. It typically consists of several stages, each designed to evaluate different aspects of a candidate's qualifications and potential contributions to the team.

1. Initial Phone Screen

The process begins with an initial phone screen, usually conducted by a recruiter. This conversation lasts about 30 minutes and focuses on understanding your background, skills, and motivations for applying to Verisk. Expect to discuss your previous experiences, your interest in the role, and how you align with the company’s values. This is also an opportunity for you to ask questions about the company culture and the specifics of the role.

2. Technical Assessment

Following the initial screen, candidates are typically required to complete a technical assessment. This may involve a combination of a written test and a practical exercise, lasting approximately three hours. The assessment is designed to evaluate your analytical skills, proficiency in data manipulation, and familiarity with tools such as SQL, Excel, and possibly Python. The tasks may include data cleaning, normalization, and analysis, reflecting the day-to-day responsibilities of a Data Analyst at Verisk.

3. Follow-Up Discussion

After the technical assessment, candidates will have a follow-up discussion with the hiring team. This conversation focuses on your performance during the assessment, where you will be asked to explain your thought process, the decisions you made, and how you would approach similar tasks in the future. This stage is crucial for demonstrating your problem-solving abilities and your capacity for self-reflection.

4. Managerial Interview

The next step typically involves an interview with the hiring manager. This session is more in-depth and may include behavioral questions aimed at understanding how you work within a team, your communication style, and your approach to collaboration. You may also be asked about your experience with specific data analysis projects and how you handle challenges in a fast-paced environment.

5. Final Interview with Senior Management

The final stage of the interview process often includes a meeting with senior management or a panel of interviewers. This round may involve more technical questions, particularly focused on statistical methods, data modeling, and your understanding of the insurance industry, given Verisk's focus. Additionally, expect to discuss your long-term career goals and how they align with the company’s mission and values.

As you prepare for your interview, consider the types of questions that may arise in each of these stages, particularly those that assess your technical skills and your fit within the company culture.

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!