Navy Federal Credit Union Data Scientist Interview Questions + Guide in 2024

Navy Federal Credit Union is a leading financial institution committed to making a meaningful impact on the lives of military members and their families. With a history of excellence and innovation, it is dedicated to providing outstanding financial services and fostering a supportive community.

Joining as a Data Scientist at Navy Federal means contributing to mission-critical organizational decision-making through cutting-edge data science. The role involves developing predictive models, analyzing large datasets, and generating insights that drive business strategy. Ideal candidates possess strong analytical skills, proficiency in programming languages like Python and SQL, and a solid understanding of statistical and machine-learning techniques.

Ready to dive in? Let’s explore the interview process, commonly asked Navy Federal Credit Union data scientist interview questions, and key tips with Interview Query to help you succeed and bag this position!

The interview process usually depends on the role and seniority; however, you can expect the following on a Navy Federal Credit Union data scientist interview:

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the Navy Federal Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

In some cases, the 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 present you with an invitation for the technical screening round. Technical screening for the Navy Federal Credit Union Data Scientist role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around data systems, ETL pipelines, and SQL queries.

In the case of Data Scientist roles, take-home assignments regarding product metrics, analytics, and data visualization are incorporated. Apart from these, your proficiency against hypothesis testing, probability distributions, and machine learning fundamentals may also be assessed during the round.

Depending on the seniority of the position, case studies and similar real-scenario problems may also be assigned.

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 Navy Federal 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 Scientist role at Navy Federal Credit Union.

Typically, interviews at Navy Federal Credit Union vary by role and team, but common data scientist interviews follow a fairly standardized process across these question topics:

1. Write a function search_list to check if a target value is in a linked list.

Write a function, search_list, that returns a boolean indicating if the target value is in the linked_list or not. You receive the head of the linked list, which is a dictionary with value and next keys. If the linked list is empty, you’ll receive None.

2. Write a query to find users who placed less than 3 orders or ordered less than $500 worth of product.

Write a query to identify the names of users who placed less than 3 orders or ordered less than $500 worth of product. Use the transactions, users, and products tables.

3. Create a function digit_accumulator to sum every digit in a string representing a floating-point number.

You are given a string that represents some floating-point number. Write a function, digit_accumulator, that returns the sum of every digit in the string.

4. Develop a function to parse the most frequent words used in poems.

You’re hired by a literary newspaper to parse the most frequent words used in poems. Poems are given as a list of strings called sentences. Return a dictionary of the frequency that words are used in the poem, processed as lowercase.

5. Write a function rectangle_overlap to determine if two rectangles overlap.

You are given two rectangles a and b each defined by four ordered pairs denoting their corners on the x, y plane. Write a function rectangle_overlap to determine whether or not they overlap. Return True if so, and False otherwise.

6. How would you design a function to detect anomalies in univariate and bivariate datasets?

If given a univariate dataset, how would you design a function to detect anomalies? What if the data is bivariate?

7. What are the drawbacks of the given student test score data layouts and how would you reformat them?

Assume you have data on student test scores in two layouts. What are the drawbacks of these layouts? What formatting changes would you make for better analysis? Describe common problems in “messy” datasets.

8. What is the expected churn rate in March for customers who bought a subscription since January 1st?

You noticed that 10% of customers who bought subscriptions in January 2020 canceled before February 1st. Assuming uniform new customer acquisition and a 20% month-over-month decrease in churn, what is the expected churn rate in March for all customers since January 1st?

9. How would you explain a p-value to a non-technical person?

Explain what a p-value is in simple terms to someone who is not technical.

10. What are Z and t-tests, and when should you use each?

Describe what Z and t-tests are, their uses, differences, and when to use one over the other.

11. How does random forest generate the forest and why use it over logistic regression?

Explain the process of how random forest generates multiple decision trees to form a forest. Discuss the advantages of using random forest over logistic regression, such as handling non-linear data and reducing overfitting.

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

Compare two machine learning algorithms. Describe scenarios where bagging (e.g., random forest) is preferred for reducing variance and boosting (e.g., AdaBoost) is preferred for reducing bias. Provide examples of tradeoffs between the two.

13. How would you evaluate and compare two credit risk models for personal loans?

1. Identify the type of model developed by the co-worker for loan approval.
2. Describe how to measure the difference between two credit risk models over a timeframe, considering monthly installment payments.
3. List metrics to track the success of the new model, such as accuracy, precision, recall, and AUC-ROC.

14. What’s the difference between Lasso and Ridge Regression?

Explain the key differences between Lasso and Ridge Regression, focusing on their regularization techniques (L1 for Lasso and L2 for Ridge) and their impact on feature selection and model complexity.

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

Describe the fundamental differences between classification models (predicting categorical outcomes) and regression models (predicting continuous outcomes). Highlight their use cases and evaluation metrics.

16. What are the Z and t-tests, and when should you use each?

Explain the purpose and differences between Z and t-tests. Describe scenarios where one test is preferred over the other.

17. What metrics would you use to evaluate the value of marketing channels?

For a company selling B2B analytics dashboards, determine which metrics are essential to assess the effectiveness and value of different marketing channels.

18. How would you determine the next partner card for a company using customer spending data?

Using customer spending data, outline the process to identify the most suitable partner for a new credit card offering.

19. How would you investigate if a redesigned email campaign led to an increase in conversion rates?

Analyze the impact of a redesigned email campaign on conversion rates, considering other potential influencing factors to ensure the observed increase is due to the campaign.

Here are some tips on how you can ace your Navy Federal Credit Union data scientist interview:

1. Master the Fundamentals: Ensure you have a strong grasp of statistics, probability, and machine learning concepts. Navy Federal looks for candidates who can drive business insights with data science.

2. Practical Experience: Brush up on your hands-on experience with SQL, R, Python, Hadoop, and other relevant tools. Be prepared to discuss past projects where you’ve solved complex problems.

3. Cultural Fit: Navy Federal values a collaborative, innovative culture. Be ready to answer behavioral questions showcasing your ability to work in teams, adapt to rapid changes, and align with the organization’s mission.

What is the average salary for a Data Scientist at Navy Federal Credit Union?

According to Glassdoor, data scientists at Navy Federal Credit Union earn between $128K to $170K per year, with an average of $147K per year.

What are the working hours and locations for the Data Scientist role?

The standard working hours are Monday to Friday, 8:30 AM - 5:00 PM. There are several potential work locations including 820 Follin Lane, Vienna, VA 22180, 5510 Heritage Oaks Drive, Pensacola, FL 32526, and 141 Security Drive, Winchester, VA 22602.

What is the company culture like at Navy Federal Credit Union?

Navy Federal values a balanced approach to work and life, recognizing the importance of both professional achievements and personal passions. The company emphasizes making a difference in military members’ and their families’ lives. The work culture is inclusive, supportive, and committed to employee growth and satisfaction.

Navy Federal Credit Union offers an exhilarating career opportunity for Data Scientists looking to delve into projects of increasing complexity within a supportive and mission-driven environment. Armed with responsibilities that span from developing predictive models to granting actionable insights, this role is ideal for those eager to make a tangible impact on the organization.

If you want more insights about the company, check out our main Navy Federal Credit Union 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 Navy Federal Credit Union’s interview process for different positions.

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!