Capital One is a well-established financial firm known for its unique and innovative approach to finance and banking. It holds a diverse and dynamic position within the financial industry. As of January 2024, Capital One ranks as the 9th largest bank in the U.S. by assets, holding a significant $468.8 billion.

Stepping into a financial firm like Capital One as a business analyst is no walk in the park. It requires strong technical skills such as data analysis, problem-solving, financial modeling, and critical thinking. As a Business Analyst at Capital One, you will explore various specialized areas within financial analysis, from credit risk to marketing to product development.

So, if you are thinking of joining this leading firm, this guide is for you. In this guide, we’ll walk you through the interview process, some commonly asked Capital One Business Analyst interview questions, and provide some valuable tips. Let’s get started!

Truist is a leading financial institution dedicated to building a better future through the merger of BB&T and SunTrust. As a hybrid work-supportive company, Truist emphasizes a collaborative and inclusive culture that nurtures professional growth and innovation.

The Data Scientist role at Truist involves performing sophisticated analytics, including statistical and predictive modeling, to provide insights that drive business outcomes and minimize risk. Responsibilities also include consulting with business leaders, leading junior data scientists, and managing various analytics projects. With a focus on machine learning, data mining, and advanced statistical techniques, the opportunity offers a dynamic environment for those passionate about transforming data into actionable strategies.

Explore this guide to understand the interview process, typical questions, and tips to navigate your journey to becoming a Data Scientist at Truist. Let's get started!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Truist as a Data Scientist. Whether you were contacted by a Truist recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the Truist 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 Truist data scientist 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 Truist data scientist role is usually conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around Truist’s data systems, machine learning models, and SQL queries.

Expect questions like:

• What are the pros and cons of KNN and logistic regression?
• Why is feature engineering used?
• How do you evaluate a model?
• What ways to adjust for over and underfitting?

Behavioral questions and no coding are typically involved in this stage, so be prepared to discuss models and case studies listed on your resume. Be ready to walk through a dashboard and explain your approach to machine learning basics.

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 Truist office. Your technical prowess, including programming and ML modeling capabilities, will be evaluated against the finalized candidates throughout these interviews.

Most candidates will face three 30-minute interviews with different interviewers covering behavioral questions, presentations, and technical interviews. Here, take-home assignments and real-scenario problems might also be discussed.

Quick Tips For Truist Data Scientist Interviews

Here are a few tips for acing your Truist interview:

• Know Your Models: Be prepared to discuss models and techniques from your resume and how you have used them in previous projects.
• Practice Behavioral Questions: Rehearse common behavioral questions and your self-introduction to ensure concise and effective communication.
• 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.

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

• 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.

• 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.

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

• Identify the type of model developed by your co-worker for loan approval.
• Explain how to measure the difference between two credit risk models over a timeframe.

• List metrics to track the success of the new model.

• What’s the difference between Lasso and Ridge Regression? Describe the key differences between Lasso and Ridge Regression techniques.

• What are the key differences between classification models and regression models? Explain the main differences between classification models and regression models.

• 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?

• 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.

• 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?

• How would you explain a p-value to a non-technical person? How would you explain what a p-value is to someone who is not technical?

• 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?

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• How would you reformat student test score data for better analysis? Given two datasets of student test scores, identify drawbacks in their current organization. Suggest formatting changes and discuss common issues in "messy" datasets.

• 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.

• 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.

• How would you investigate if a redesigned email campaign led to increased conversion rates? Analyze the impact of a redesigned email campaign on conversion rates, considering other potential influencing factors.

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

Q: What is the interview process for a Data Scientist position at Truist like?
The interview process at Truist typically includes two rounds of interviews, each combining technical and behavioral questions. Fast and concise answers are expected as there are multiple questions within a given time frame. The technical questions often focus on machine learning basics, model evaluation, SQL, and specific models listed on your resume. No coding is involved.

Q: 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.

Q: What can I expect regarding 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.

Q: What type of questions should I prepare for during the Truist Data Scientist interview?
Be prepared for a mix of behavioral and technical questions. Behavioral questions often require a brief self-introduction and "tell me about a time" scenarios. Technical questions will revolve around the models mentioned in your resume, their pros and cons, feature engineering, model evaluations, and handling overfitting and underfitting.

Q: 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. Depending on the position, additional benefits such as a defined benefit pension plan and restricted stock units may be available.

Looking to dive deep into the realm of data science at a forward-thinking financial institution? 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. The interview process at Truist is streamlined and straightforward, focusing on both technical proficiency and behavioral qualities, ensuring you demonstrate the full spectrum of your expertise.

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

Check out all our 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! ????