LexisNexis Data Scientist Interview Questions + Guide in 2024

LexisNexis Risk Solutions, serving customers in over 150 countries, is a leading global legal, regulatory, and business information provider. Part of RELX Group, LexisNexis leverages advanced analytics and decision tools to help customers increase productivity, improve decision-making, and achieve impactful outcomes.

At LexisNexis, data scientists play a crucial role in analyzing and manipulating large datasets to develop predictive models and methodologies that inform business strategies. They collaborate with cross-functional teams to clarify project objectives and enhance stakeholder understanding while creating tools to streamline data processes. Additionally, their ability to communicate complex insights through visual storytelling ensures that data-driven findings are accessible and impactful for decision-making.

In this guide, we’ll tackle how they conduct their data science interviews, along with commonly asked LexisNexis data scientist interview questions to help you prepare better.

The interview process usually depends on the role and seniority. However, you can expect the following on a LexisNexis data scientist interview:

Initial Phone Screening

If your application is shortlisted, you’ll be contacted by a recruiter from LexisNexis for an initial phone screening. This screening typically involves verifying critical details about your experience and skill level. Expect some behavioral questions during this round.

Sometimes, the hiring manager might also join the call to answer your questions about the role and company and engage in preliminary technical and behavioral discussions.

This initial phone call typically lasts about 30 minutes.

Online Assessments

Once you clear the phone screening, you’ll be invited to complete an online assessment, which often includes a Hackerrank test. This stage aims to evaluate your core data science and programming skills.

Moreover, you may receive a take-home assessment focusing on specific data analysis or machine learning aspects relevant to the job.

Technical Virtual Interview

After successfully navigating the online assessments, you’ll move on to a technical virtual interview. This interview usually takes place via video conferencing and lasts around 1 hour. The focus of this round could include:

• Detailed discussions on your take-home assessment
• Technical questions on topics like ML algorithms, NLP, and SQL queries
• Evaluating your problem-solving approaches and analytical skills

Onsite or Virtual Onsite Interview Rounds

Following another recruiter call to outline the following stages, you will be invited for multiple onsite or virtual interview rounds. These may involve one-on-one interviews, group exercises, and technical presentations. You might also need to display your problem-solving abilities through coding exercises and answer questions about your take-home assessment.

Sometimes, team members from cross-functional areas, such as product teams, could be part of these interactions.

Typically, interviews at LexisNexis Risk Solutions vary by role and team, but commonly Data Scientist interviews follow a fairly standardized process across these question topics.

1. Write 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. Create a function is_subsequence to determine if one string is a subsequence of another.

Given two strings, string1 and string2, write a function is_subsequence to determine 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 prime numbers up to N. Return an empty list if no prime numbers are 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 from scratch, to sort the list in ascending alphabetical order. Do not use the built-in sorted function and return the new sorted list rather than modifying the list in place.

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

Explain the concept of a p-value in simple terms to someone without a technical background. Use analogies or everyday examples to make it understandable.

7. 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 shown to you was pulled from Bucket #1.

8. What is the probability that Amy wins the game by rolling a 6 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 6 before Brad does.

9. How would you evaluate whether using a decision tree algorithm is the correct model for predicting loan repayment?

You are tasked with building a decision tree model to predict whether a borrower will repay a personal loan. How would you evaluate if a decision tree is the right choice, and how would you assess its performance before and after deployment?

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

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

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

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

12. How would you determine if you have enough data to create 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 assess if this data is sufficient for an accurate model?

13. 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 develop a model to predict which merchants the company should target for acquisition when entering a new market?

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

Jetco had the fastest average boarding times in a study. Identify potential biases in the study and what factors you would investigate to ensure accurate results.

15. How would you ensure data quality across different ETL platforms for PayPal’s Southern African survey data?

PayPal uses multiple ETL pipelines to connect data marts with survey platform data warehouses, including translation modules for text data. Describe how you would ensure data quality across these platforms.

16. How would you debug the marriage attribute marked ‘TRUE’ for all auto insurance clients?

You find that the marriage attribute is marked ‘TRUE’ for all auto insurance clients. Explain how you would debug this issue, what data you would examine, and how you would determine the actual marital status of the clients.

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 LexisNexis data scientist interview include:

• Prepare for Technical Depth: Ensure you are well-versed in machine learning techniques, NLP tools, and big data technologies. Brush up on Python, R, and relevant data science tools.
• Collaborative Mindset: LexisNexis values cross-functional collaboration. Be ready to discuss examples from past experiences where you worked with various teams to align AI solutions with business goals.
• Show Your Problem-Solving Skills: Be prepared for case studies and real-scenario problems. Demonstrating a logical and structured approach to problem-solving will be beneficial.

What is the average salary for a Data Scientist at Lexisnexis?

What skills and qualifications are required for a Data Scientist at LexisNexis?

Candidates typically need an advanced degree in Computer Science, Mathematics, Statistics, or a related field. Key skills include proficiency in big data technologies (Hadoop, Spark, AWS), experience with large language models (BERT, RoBERTa, T5), and comfort in programming languages such as Python or R. Machine learning expertise, including algorithms like gradient boosting and random forests, is also crucial.

What is the work culture like at LexisNexis?

LexisNexis promotes a healthy work/life balance with flexible work hours and various wellbeing initiatives. Employees are encouraged to work in a way that suits them best to enhance productivity. The company supports professional growth through coaching and mentoring, making it an excellent place for career development.

What benefits does LexisNexis offer to its employees?

LexisNexis provides a comprehensive benefits package, including health insurance, retirement benefits like a 401(k) with match, wellness programs, and various family benefits such as adoption and surrogacy support. The company also offers health savings accounts, numerous well-being initiatives, and paid leave for participating in employee resource groups or volunteer work.

LexisNexis is looking for visionary and dynamic Data Scientists to join its team as the legal and data landscape continues to evolve. With a robust interview process designed to identify the best and brightest, LexisNexis offers an opportunity to grow your career, contribute to cutting-edge AI and machine learning projects, and lead initiatives directly impacting global business solutions.

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

Good luck with your interview!