Citizens Financial Group, Inc. Data Scientist Interview Questions + Guide in 2025

Citizens Financial Group, Inc. is one of the oldest and largest financial institutions in the United States, dedicated to providing a wide range of banking products and services to individuals and businesses alike.

As a Data Scientist at Citizens, you will play a pivotal role in leveraging advanced analytics to drive insights and support compliance efforts, particularly in areas such as Anti-Money Laundering (AML) and regulatory compliance. Your responsibilities will include developing and calibrating predictive models using machine learning and statistical methods, collaborating with cross-functional teams to analyze complex datasets, and providing actionable insights that inform strategic business decisions. A strong understanding of statistical concepts, programming skills in languages such as Python and SQL, and the ability to communicate complex analyses effectively to non-technical stakeholders are crucial for success in this role. Additionally, familiarity with the banking industry and regulatory requirements will significantly enhance your contributions to the team.

This guide will help you prepare for your interview by providing a comprehensive understanding of the role and its alignment with Citizens’ values, ensuring you can articulate your relevant experience and demonstrate your fit for the position.

The interview process for a Data Scientist role at Citizens Financial Group is structured to assess both technical and interpersonal skills, ensuring candidates align with the company’s values and objectives. The process typically consists of several key stages:

1. Initial Screening

The first step is an initial screening, usually conducted by a recruiter over the phone. This conversation lasts about 30 minutes and focuses on your background, experience, and motivations for applying to Citizens. The recruiter will also provide insights into the company culture and the specifics of the Data Scientist role, allowing you to gauge your fit within the organization.

2. Behavioral Interview

Following the initial screening, candidates typically participate in a behavioral interview. This interview is designed to assess your past experiences and how they relate to the responsibilities of the Data Scientist position. Expect a friendly atmosphere where you will be asked to discuss your previous projects, teamwork experiences, and how you handle challenges. The interviewer will be interested in understanding your thought process and how you communicate complex ideas to non-technical stakeholders.

3. Technical Assessment

The next stage involves a technical assessment, which may be conducted via a video call. This assessment focuses on your analytical skills and technical knowledge relevant to data science. You may be asked to solve problems related to statistical analysis, machine learning, or data modeling. Be prepared to demonstrate your proficiency in programming languages such as Python or R, as well as your understanding of data manipulation and visualization techniques.

4. Onsite Interview

If you successfully pass the previous stages, you will be invited for an onsite interview. This typically consists of multiple rounds with different team members, including senior data scientists and business stakeholders. Each round will delve deeper into your technical expertise, problem-solving abilities, and how you approach data-driven decision-making. You may also be asked to present a case study or a project you have worked on, showcasing your analytical skills and ability to derive actionable insights from data.

5. Final Interview

The final interview often involves discussions with higher management or team leads. This stage is less technical and more focused on cultural fit, leadership potential, and your long-term career aspirations within the company. You will have the opportunity to ask questions about the team dynamics, ongoing projects, and the company’s vision for the future.

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 skills and past experiences.

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

Embrace the Behavioral Focus

Citizens Bank places a strong emphasis on behavioral interviews, as indicated by previous candidates’ experiences. Prepare to discuss your past experiences in data science projects and how they relate to the role. Be ready to articulate your thought process, decision-making, and how you’ve collaborated with others. This is your chance to showcase not just your technical skills, but also your interpersonal abilities and how you fit into the company culture.

Prepare for a Friendly Atmosphere

Candidates have noted that the interview atmosphere is generally relaxed and friendly. Approach the interview with a positive attitude and be yourself. This will help you build rapport with your interviewers. Remember, they are not just assessing your skills but also your fit within the team and the company culture. A personable demeanor can go a long way in making a lasting impression.

Highlight Your Technical Proficiency

While the interviews may lean towards behavioral questions, it’s essential to demonstrate your technical expertise. Be prepared to discuss your experience with machine learning, statistical analysis, and data modeling. Familiarize yourself with the specific tools and languages mentioned in the job description, such as SQL, Python, and R. Even if technical questions are not a primary focus, showing that you have a solid foundation in these areas will reinforce your qualifications.

Understand the Regulatory Environment

Given the role’s focus on Anti Money Laundering (AML) and compliance, it’s crucial to have a basic understanding of the regulatory landscape. Familiarize yourself with the Bank Secrecy Act and the US Patriot Act, as well as how they relate to data science in the banking sector. This knowledge will not only help you answer questions more effectively but also demonstrate your commitment to the role and the industry.

Communicate Clearly and Effectively

Strong verbal communication skills are essential for this role, especially when conveying complex data insights to non-technical stakeholders. Practice explaining your past projects and findings in a clear and concise manner. Use storytelling techniques to make your experiences relatable and engaging. This will help you stand out as a candidate who can bridge the gap between technical analysis and business strategy.

Build Relationships and Collaborate

Citizens Bank values collaboration across teams. Be prepared to discuss how you have successfully worked with cross-functional teams in the past. Highlight specific examples where you built relationships with business partners to achieve common goals. This will demonstrate your ability to work effectively within the organization and contribute to a collaborative culture.

Show Enthusiasm for Continuous Learning

The field of data science is constantly evolving, and Citizens Bank is looking for candidates who are eager to learn and adapt. Share examples of how you stay updated with the latest trends and technologies in data science. This could include attending workshops, participating in online courses, or engaging with professional communities. Your enthusiasm for continuous improvement will resonate well with interviewers.

Conclude with Questions

At the end of the interview, take the opportunity to ask insightful questions about the team, projects, and company culture. This not only shows your interest in the role but also helps you assess if Citizens Bank is the right fit for you. Tailor your questions based on your research about the company and the specific challenges they face in the data science domain.

By following these tips, you will be well-prepared to make a strong impression during your interview at Citizens Bank. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Citizens Financial Group. The interview process will likely focus on a blend of technical skills, statistical knowledge, and behavioral aspects, particularly in relation to risk management and compliance. Candidates should be prepared to discuss their experience with machine learning, data analysis, and their ability to communicate complex findings to non-technical stakeholders.

Machine Learning

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

This question aims to assess your practical experience with machine learning and its application in real-world scenarios.

How to Answer

Discuss the project’s objectives, the machine learning techniques you employed, and the results achieved. Highlight any metrics that demonstrate the project’s success.

Example

“I worked on a project to develop a predictive model for identifying potential money laundering activities. By utilizing logistic regression and decision trees, we improved detection rates by 30%, which significantly reduced false positives and enhanced compliance efforts.”

2. What machine learning algorithms are you most comfortable with, and why?

This question evaluates your familiarity with various algorithms and your ability to choose the right one for a given problem.

How to Answer

Mention specific algorithms, explain their strengths and weaknesses, and provide examples of when you have used them.

Example

“I am most comfortable with random forests and gradient boosting machines due to their robustness and ability to handle non-linear relationships. For instance, I used random forests to analyze customer transaction data, which helped in identifying patterns indicative of fraud.”

3. How do you handle overfitting in your models?

This question tests your understanding of model evaluation and improvement techniques.

How to Answer

Discuss techniques such as cross-validation, regularization, and pruning. Provide examples of how you have applied these methods in past projects.

Example

“To prevent overfitting, I typically use cross-validation to assess model performance on unseen data. In a recent project, I applied L1 regularization to reduce the complexity of my model, which improved its generalization on the validation set.”

4. Explain the concept of feature engineering and its importance.

This question assesses your knowledge of data preprocessing and its impact on model performance.

How to Answer

Define feature engineering and discuss its role in improving model accuracy. Provide examples of features you have engineered in past projects.

Example

“Feature engineering involves creating new input features from existing data to improve model performance. For example, in a credit risk model, I created features like the ratio of debt to income, which significantly enhanced the model’s predictive power.”

5. How do you evaluate the performance of your machine learning models?

This question evaluates your understanding of model evaluation metrics.

How to Answer

Discuss various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC. Explain how you choose the appropriate metric based on the problem context.

Example

“I evaluate model performance using precision and recall, especially in fraud detection scenarios where false positives can be costly. For instance, I monitored the F1 score to balance precision and recall, ensuring that our model was effective in identifying fraudulent transactions without overwhelming the team with false alerts.”

Statistics & Probability

1. Can you explain the difference between Type I and Type II errors?

This question tests your understanding of statistical hypothesis testing.

How to Answer

Define both types of errors and provide examples of their implications in a business context.

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. In the context of fraud detection, a Type I error could mean flagging a legitimate transaction as fraudulent, while a Type II error could result in missing an actual fraudulent transaction.”

2. What statistical methods do you use for data analysis?

This question assesses your familiarity with statistical techniques.

How to Answer

Mention specific methods such as regression analysis, hypothesis testing, or time series analysis, and provide examples of their application.

Example

“I frequently use regression analysis to understand relationships between variables. For instance, I applied multiple regression to analyze the impact of various factors on loan default rates, which helped in refining our risk assessment models.”

3. How do you handle missing data in your datasets?

This question evaluates your data cleaning and preprocessing skills.

How to Answer

Discuss techniques such as imputation, deletion, or using algorithms that handle missing values. Provide examples of how you have addressed missing data in past projects.

Example

“I handle missing data by first assessing the extent and pattern of the missingness. In a recent project, I used mean imputation for numerical features and mode imputation for categorical features, which allowed me to retain a significant portion of the dataset without introducing bias.”

4. Describe a time when you had to explain complex statistical concepts to a non-technical audience.

This question assesses your communication skills and ability to simplify complex information.

How to Answer

Provide a specific example where you successfully communicated statistical findings to stakeholders.

Example

“I presented the results of a risk assessment model to our compliance team, focusing on the key metrics and their implications. I used visual aids to illustrate the data trends, which helped them understand the model’s effectiveness in identifying high-risk customers.”

5. What is the Central Limit Theorem, and why is it important?

This question tests your foundational knowledge of statistics.

How to Answer

Define the Central Limit Theorem and explain its significance in statistical inference.

Example

“The Central Limit Theorem states that the distribution of the sample means approaches a normal distribution as the sample size increases, regardless of the population’s distribution. This is crucial for making inferences about population parameters based on sample statistics, especially in risk modeling.”

Behavioral Questions

1. Describe a challenging data science project you worked on and how you overcame the challenges.

This question assesses your problem-solving skills and resilience.

How to Answer

Discuss the project, the specific challenges faced, and the strategies you employed to overcome them.

Example

“I worked on a project to develop a model for predicting customer churn. The challenge was dealing with imbalanced classes. I overcame this by using SMOTE for oversampling the minority class and adjusting the model’s threshold, which improved our prediction accuracy significantly.”

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

This question evaluates your time management and organizational skills.

How to Answer

Discuss your approach to prioritization, including any frameworks or tools you use.

Example

“I prioritize tasks based on their impact and urgency. I use a Kanban board to visualize my workload and ensure that I focus on high-impact projects first. This approach has helped me manage multiple deadlines effectively while maintaining quality.”

3. Can you give an example of how you collaborated with cross-functional teams?

This question assesses your teamwork and collaboration skills.

How to Answer

Provide a specific example of a project where you worked with other teams and the outcomes of that collaboration.

Example

“I collaborated with the IT and compliance teams to implement a new data governance framework. By aligning our goals and sharing insights, we successfully established protocols that improved data quality and compliance with regulatory standards.”

4. How do you stay updated with the latest trends and technologies in data science?

This question evaluates your commitment to continuous learning.

How to Answer

Discuss the resources you use to stay informed, such as online courses, webinars, or professional networks.

Example

“I regularly attend data science meetups and webinars, and I follow industry leaders on platforms like LinkedIn. Additionally, I take online courses to learn about emerging technologies, such as generative AI, which I believe will play a significant role in the future of data science.”

5. Describe a situation where you had to make a decision with incomplete data.

This question assesses your decision-making skills under uncertainty.

How to Answer

Discuss the context, the decision made, and the rationale behind it.

Example

“During a project to assess credit risk, I had incomplete data on some applicants. I decided to proceed with a conservative approach by applying stricter criteria based on available data. This decision minimized potential risk while we worked on gathering the missing information.”