Truist Data Scientist Interview Questions + Guide in 2025

Truist is a leading financial services company committed to providing innovative solutions that enhance customer experiences and drive business success.

As a Data Scientist at Truist, you will be at the forefront of leveraging sophisticated analytics to derive actionable insights that improve business outcomes and minimize risk. This role encompasses a diverse range of responsibilities, including conducting statistical and predictive analyses, developing machine learning models, and collaborating with business leaders to integrate analytics into strategic initiatives. You will be expected to manage end-to-end data science solutions, ensuring technical delivery aligns with measurable business results.

A successful candidate should possess a strong foundation in statistical methods and programming languages such as Python, R, or SAS, alongside experience with data visualization tools and frameworks like SQL and Hadoop. Additionally, excellent communication skills are essential for translating complex analytical findings to various stakeholders, fostering strong relationships, and guiding junior team members in their professional growth.

Truist values innovation and collaboration, making it crucial for candidates to demonstrate adaptability and a willingness to explore emerging technologies and methodologies in the data science field. This guide will help you prepare by providing insights into the expectations and competencies desired for this role, allowing you to present yourself as a well-qualified candidate during your interview.

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Truist. The interview process will likely assess both your technical skills and your ability to work collaboratively in a diverse environment. Be prepared to discuss your experience with data analytics, machine learning, and statistical methods, as well as your approach to problem-solving and teamwork.

Machine Learning

1. Can you explain the pros and cons of K-Nearest Neighbors (KNN) and Logistic Regression?

Understanding the strengths and weaknesses of different algorithms is crucial for a data scientist.

How to Answer

Discuss the scenarios where each algorithm excels and their limitations, focusing on aspects like interpretability, computational efficiency, and data requirements.

Example

"KNN is great for non-linear data and is easy to understand, but it can be computationally expensive with large datasets. Logistic Regression, on the other hand, is efficient and interpretable, making it suitable for binary classification, but it assumes a linear relationship between features and the target variable."

2. How do you evaluate the performance of a machine learning model?

This question tests your understanding of model evaluation metrics.

How to Answer

Mention various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC, and explain when to use each.

Example

"I evaluate model performance using multiple metrics. For classification tasks, I often look at accuracy and F1 score to balance precision and recall. For binary classification, I also consider the ROC-AUC score to assess the model's ability to distinguish between classes."

3. What techniques do you use to prevent overfitting in your models?

This question assesses your knowledge of model generalization.

How to Answer

Discuss techniques like cross-validation, regularization, and pruning, and explain how they help improve model performance.

Example

"I use cross-validation to ensure my model generalizes well to unseen data. Additionally, I apply regularization techniques like L1 and L2 to penalize overly complex models, which helps prevent overfitting."

4. Can you describe a machine learning project you worked on and the impact it had?

This question allows you to showcase your practical experience.

How to Answer

Outline the problem, your approach, the algorithms used, and the results achieved.

Example

"I worked on a customer churn prediction model using logistic regression. By analyzing customer behavior data, we identified key factors contributing to churn. The model helped the marketing team implement targeted retention strategies, reducing churn by 15% over six months."

Statistics & Probability

1. What is a confusion matrix, and how do you interpret it?

This question tests your understanding of model evaluation in classification tasks.

How to Answer

Explain what a confusion matrix is and how to derive metrics like accuracy, precision, recall, and F1 score from it.

Example

"A confusion matrix is a table that summarizes the performance of a classification model by showing true positives, true negatives, false positives, and false negatives. From this, I can calculate precision, recall, and F1 score to evaluate the model's effectiveness."

2. How do you handle missing data in a dataset?

This question assesses your data preprocessing skills.

How to Answer

Discuss various strategies such as imputation, deletion, or using algorithms that support missing values.

Example

"I handle missing data by first analyzing the extent and pattern of the missingness. Depending on the situation, I might use imputation techniques like mean or median substitution, or if the missing data is substantial, I may consider removing those records altogether."

3. Can you explain the concept of p-values and their significance in hypothesis testing?

This question evaluates your understanding of statistical inference.

How to Answer

Define p-values and explain their role in determining statistical significance.

Example

"A p-value indicates the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A low p-value (typically < 0.05) suggests that we can reject the null hypothesis, indicating that the observed effect is statistically significant."

4. What is the difference between Type I and Type II errors?

This question tests your knowledge of hypothesis testing.

How to Answer

Define both types of errors and provide examples of each.

Example

"A Type I error occurs when we reject a true null hypothesis, while a Type II error happens when we fail to reject a false null hypothesis. For instance, concluding that a new drug is effective when it is not is a Type I error, whereas failing to detect its effectiveness when it is effective is a Type II error."

Data Visualization

1. How do you approach data visualization to communicate insights effectively?

This question assesses your ability to present data clearly.

How to Answer

Discuss the importance of clarity, audience consideration, and the tools you use for visualization.

Example

"I prioritize clarity and simplicity in my visualizations, ensuring that the audience can easily grasp the insights. I often use tools like Tableau or Matplotlib to create interactive dashboards that highlight key metrics and trends relevant to the stakeholders."

2. Can you describe a time when your data visualization influenced a business decision?

This question allows you to demonstrate the impact of your work.

How to Answer

Share a specific example where your visualization led to actionable insights.

Example

"I created a dashboard that visualized customer feedback trends over time. By highlighting recurring issues, the product team was able to prioritize feature updates, resulting in a 20% increase in customer satisfaction scores."

3. What are some common pitfalls in data visualization that you try to avoid?

This question tests your awareness of best practices in visualization.

How to Answer

Mention issues like clutter, misleading scales, and lack of context.

Example

"I avoid cluttered visuals that can confuse the audience. I also ensure that scales are consistent and not misleading, and I always provide context to help the audience understand the significance of the data presented."

4. How do you ensure your visualizations are accessible to a diverse audience?

This question assesses your commitment to inclusivity in data presentation.

How to Answer

Discuss the importance of color choices, labeling, and providing alternative formats.

Example

"I use color palettes that are colorblind-friendly and ensure that all visuals are well-labeled. Additionally, I provide alternative text descriptions for key insights to make the information accessible to all audience members."

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

Prepare for Behavioral and Technical Questions

Truist places a strong emphasis on both behavioral and technical competencies during the interview process. Be ready to discuss your past experiences in detail, particularly those that demonstrate your ability to work in diverse teams and handle complex projects. Prepare specific examples that showcase your problem-solving skills, leadership abilities, and how you’ve contributed to team success. Additionally, familiarize yourself with the technical aspects of your resume, as interviewers will likely ask about the models and tools you’ve listed.

Master SQL and Machine Learning Concepts

Given the focus on SQL and machine learning in the interviews, ensure you have a solid understanding of these areas. Brush up on SQL queries, especially those involving data manipulation and extraction. Be prepared to discuss machine learning concepts, including the pros and cons of various algorithms like KNN and logistic regression, as well as model evaluation techniques. Understanding how to adjust for overfitting and underfitting will also be crucial.

Showcase Your Analytical Skills

During the interview, you may be asked to walk through a dashboard or present data insights. Practice explaining your analytical process clearly and concisely. Highlight how you derive actionable insights from data and how those insights can impact business outcomes. Be ready to discuss specific projects where your analytical skills led to measurable improvements.

Emphasize Collaboration and Mentorship

Truist values teamwork and mentorship within its data science teams. Be prepared to discuss how you’ve collaborated with others in past roles and how you’ve contributed to the growth of junior team members. Share examples of how you’ve fostered a positive team environment and helped others succeed, as this aligns with the company’s culture of inclusivity and support.

Understand the Company Culture

Truist is known for its friendly and collaborative work environment. Research the company’s values and mission to ensure your responses align with their culture. Demonstrating an understanding of Truist’s commitment to diversity and community engagement can set you apart. Be genuine in expressing why you want to be part of their team and how you can contribute to their goals.

Practice Your Presentation Skills

If your interview includes a presentation component, practice delivering your insights confidently. Focus on clarity and engagement, ensuring that your audience can easily follow your thought process. Use visual aids effectively to support your points, and be prepared to answer questions that may arise during or after your presentation.

Follow Up Thoughtfully

After your interview, send a thoughtful thank-you note to your interviewers. Mention specific topics discussed during the interview to reinforce your interest in the role and the company. This not only shows your appreciation but also keeps you top of mind as they make their decision.

By following these tips and preparing thoroughly, you’ll position yourself as a strong candidate for the Data Scientist role at Truist. Good luck!

The interview process for a Data Scientist role at Truist is structured to assess both technical and behavioral competencies, ensuring candidates are well-rounded and fit for the company's culture and expectations.

1. Initial Screening

The process begins with an initial screening, typically conducted via a 30-minute video interview with a recruiter. This conversation focuses on your background, skills, and motivations for applying to Truist. The recruiter will also gauge your fit within the company culture and discuss the role's expectations.

2. Technical and Behavioral Interview

Following the initial screening, candidates will participate in a technical and behavioral interview. This round may involve a mix of questions related to your experience with data analytics, machine learning, and statistical methods. You should be prepared to discuss specific models and techniques you have used, as well as how you approach problem-solving in a team environment. Expect to articulate your understanding of SQL, data visualization, and the tools you have employed in past projects.

3. Superday Interviews

Candidates who successfully pass the previous rounds will be invited to a Superday, which consists of multiple interviews in one day. This typically includes three 30-minute sessions with different interviewers. The first session often focuses on behavioral questions, where you will need to provide examples of past experiences that demonstrate your skills and teamwork. The second session may require you to present a project or case study, showcasing your analytical capabilities and communication skills. The final session will delve into technical questions, assessing your knowledge of machine learning concepts, model evaluation, and data handling techniques.

4. Final Assessment

In some cases, there may be a final assessment or follow-up interview to clarify any outstanding questions from the previous rounds. This could involve deeper discussions about your technical skills or further exploration of your fit within the team and company.

As you prepare for your interview, consider the types of questions that may arise in each of these stages, particularly those that relate to your technical expertise and past experiences.

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

Explain how random forest generates multiple decision trees and why it might be preferred over logistic regression in certain scenarios.

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

Compare two machine learning algorithms and provide examples of tradeoffs between using bagging and boosting algorithms.

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

1. Identify the type of model developed by your co-worker for loan approval.
2. Explain how to measure the difference between two credit risk models over time.
3. List metrics to track the success of the new model.

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

Describe the key differences between Lasso and Ridge Regression techniques.

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

Explain the main differences between classification models and regression models.

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

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

7. What are the drawbacks of the given student test score data layouts?

Assume you have data on student test scores in two layouts (dataset 1 and dataset 2). 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 the product 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 who bought the product since January 1st?

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

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

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

What are the Z and t-tests? What are they used for? What is the difference between them? When should you use one over the other?

11. 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 keys value and next. If the linked list is empty, you’ll receive None.

12. 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.

13. 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.

14. 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 of words used in the poem, processed as lowercase.

15. 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.

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

Given data on marketing channels and costs for a B2B analytics dashboard company, identify key metrics to determine each channel’s value.

17. How would you determine the next partner card using customer spending data?

With access to customer spending data, outline the process to identify the best partner for a new credit card offering.

18. How would you investigate whether a redesigned email campaign increased conversion rates?

Analyze the impact of a redesigned email campaign on conversion rates, considering other potential influencing factors.

Here are a few tips for acing your Truist interview:

1. Know Your Models: Be prepared to discuss models and techniques from your resume and how you have used them in previous projects.

2. Practice Behavioral Questions: Rehearse common behavioral questions and your self-introduction to ensure concise and effective communication.

3. Brush Up on SQL and ML Basics: Expect questions on SQL and machine learning fundamentals, especially how you handle model evaluation, feature engineering, and adjusting for overfitting and underfitting.

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

What essential skills and qualifications should a Data Scientist at Truist possess?

A Data Scientist at Truist should have a Bachelor’s degree in a quantitative field such as Finance, Mathematics, Analytics, Data Science, Computer Science, or Engineering. Additionally, six years of related experience and expertise in areas relevant to banking and financial services are required. Proficiency in statistical programming (Python, R, SAS), machine learning, and data tools like Hadoop, Pig, and Spark is essential.

What can I expect regarding the work environment and schedule at Truist?

The Data Scientist position at Truist supports a hybrid work schedule based on current company guidance. The role involves performing sophisticated data analytics to provide actionable insights and minimize risk. You will also consult with business leaders, manage analytics initiatives, and mentor junior data scientists to foster their growth.

What additional benefits does Truist offer to its employees?

Truist offers a range of benefits, including medical, dental, vision, life insurance, disability, tax-preferred savings accounts, and a 401k plan. Employees also receive vacation days, sick days, and paid holidays. Additional benefits, such as a defined benefit pension plan and restricted stock units, may be available depending on the position.

Truist offers a unique opportunity for data scientists to leverage sophisticated analytics, machine learning, and statistical methodologies to drive impactful business outcomes and mitigate risks.

Excited to learn more about what it takes to excel at Truist? Discover more insights and get prepared with our comprehensive resources. Using our main Truist interview guide, we are dedicated to equipping you with the essential tools, confidence, and strategies needed to tackle every challenge and ace your Truist interview.

Check out all our other company interview guides for enhanced preparation. If you have any questions or need further assistance, don’t hesitate to reach out.

Good luck with your interview journey!