BNY Mellon Machine Learning Engineer Interview Questions + Guide in 2025

BNY Mellon is a global investments company dedicated to helping clients manage and service their financial assets.

As a Machine Learning Engineer at BNY Mellon, you will be responsible for designing, developing, and deploying machine learning models to improve financial services and optimize investment strategies. Key responsibilities include collaborating with data scientists and analysts to understand their needs, implementing algorithms and models, and ensuring the scalability and reliability of machine learning solutions. Strong programming skills in languages such as Python and Java, along with a solid understanding of data structures, algorithms, and machine learning frameworks (like TensorFlow or PyTorch), are essential for success in this role. Ideal candidates will possess experience in deploying machine learning models in production environments and demonstrate an analytical mindset with a knack for problem-solving.

This guide will help you prepare for your interview by providing insights into the role's expectations and the types of questions you can expect, ultimately giving you a competitive edge in the hiring process.

The interview process for a Machine Learning Engineer at BNY Mellon is structured and thorough, designed to assess both technical skills and cultural fit. The process typically unfolds in several key stages:

1. Initial Screening

The first step involves an initial screening, which is often conducted by a recruiter. This stage typically lasts around 30 minutes and focuses on understanding your background, experience, and motivation for applying to BNY Mellon. Expect to discuss your resume, previous projects, and general behavioral questions to gauge your fit within the company culture.

2. Online Assessment

Following the initial screening, candidates usually undergo an online assessment. This assessment often includes coding challenges that test your knowledge of data structures and algorithms. The questions can range from easy to hard levels, and candidates are typically required to solve multiple problems within a set timeframe. This stage serves as a filter to identify candidates who possess the necessary technical skills for the role.

3. Technical Interviews

Candidates who pass the online assessment are invited to participate in one or more technical interviews. These interviews are typically conducted by experienced engineers and focus on a variety of topics, including machine learning concepts, programming languages (such as Python or Java), and relevant frameworks. Expect to encounter questions that require you to demonstrate your problem-solving abilities through live coding exercises, as well as discussions about your past projects and their technical details.

4. Behavioral Interview

In addition to technical assessments, candidates will also face a behavioral interview. This round is designed to evaluate your soft skills, teamwork, and how you handle various workplace scenarios. Interviewers may use the STAR (Situation, Task, Action, Result) method to prompt you to share specific examples from your past experiences that highlight your interpersonal skills and adaptability.

5. Final Interview

The final stage often involves a wrap-up interview with a hiring manager or a senior team member. This round may include a mix of technical and behavioral questions, as well as discussions about your career aspirations and how they align with the goals of BNY Mellon. This is also an opportunity for you to ask questions about the team, projects, and company culture.

As you prepare for your interview, be ready to tackle a variety of questions that reflect both your technical expertise and your ability to work collaboratively in a team environment.

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

Understand the Interview Structure

The interview process at BNY Mellon typically consists of multiple rounds, including a coding assessment followed by technical interviews and an HR round. Familiarize yourself with this structure and prepare accordingly. Expect to face a mix of coding questions, data structures and algorithms (DSA), and behavioral questions. Knowing the flow of the interview will help you manage your time and responses effectively.

Master Data Structures and Algorithms

Given the emphasis on coding assessments, ensure you have a solid grasp of data structures and algorithms. Practice common problems on platforms like LeetCode, focusing on medium to hard difficulty levels. Be prepared to explain your thought process and the time and space complexity of your solutions. Review concepts such as sliding windows, graphs, and recursion, as these have been frequently mentioned in past interviews.

Prepare for Behavioral Questions

Behavioral questions are a significant part of the interview process. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Be ready to discuss your previous projects, how you handled challenges, and your teamwork experiences. BNY Mellon values candidates who can articulate their experiences clearly and demonstrate their problem-solving skills in real-world scenarios.

Showcase Your Projects

Your resume will likely be a focal point during the interviews, so be prepared to discuss your projects in detail. Highlight your role, the technologies used, and the impact of your work. Interviewers often ask about specific projects, so ensure you can explain your contributions and the outcomes effectively.

Be Ready for Technical Depth

Expect in-depth technical questions related to your expertise, especially in machine learning, programming languages, and frameworks relevant to the role. Brush up on your knowledge of Java, Spring Boot, and any other technologies mentioned in your resume. Be prepared to write code live and explain your reasoning as you go.

Emphasize Adaptability and Learning

BNY Mellon values candidates who can adapt to new challenges and demonstrate a willingness to learn. Be prepared to discuss how you stay updated with industry trends and technologies. Share examples of how you have adapted to changes in your work environment or learned new skills to meet project demands.

Ask Insightful Questions

At the end of your interview, take the opportunity to ask thoughtful questions about the team, company culture, and future projects. This not only shows your interest in the role but also helps you gauge if BNY Mellon is the right fit for you. Questions about team dynamics, growth opportunities, and the company's approach to innovation can provide valuable insights.

Stay Calm and Confident

Finally, remember to stay calm and confident throughout the interview process. Practice mock interviews to build your confidence and improve your communication skills. A positive attitude and a clear, concise way of expressing your thoughts can make a significant difference in how you are perceived by the interviewers.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Machine Learning Engineer role at BNY Mellon. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at BNY Mellon. The interview process will likely assess your technical skills in machine learning, data structures, algorithms, and your ability to work collaboratively in a team environment. Be prepared to discuss your past projects and experiences in detail, as well as demonstrate your problem-solving abilities through coding challenges.

Machine Learning

1. Can you explain the difference between supervised and unsupervised learning?

Understanding the fundamental concepts of machine learning is crucial. Be clear about the definitions and provide examples of each type.

How to Answer

Discuss the characteristics of both supervised and unsupervised learning, including the types of problems they solve and the data they use.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as predicting house prices based on features like size and location. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns or groupings, like clustering customers based on purchasing behavior.”

2. Describe a machine learning project you have worked on. What challenges did you face?

This question assesses your practical experience and problem-solving skills.

How to Answer

Outline the project scope, your role, the challenges encountered, and how you overcame them.

Example

“I worked on a project to predict customer churn for a telecom company. One challenge was dealing with imbalanced data. I implemented techniques like SMOTE to generate synthetic samples for the minority class, which improved our model's performance significantly.”

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

This question tests your understanding of model evaluation metrics.

How to Answer

Discuss various metrics and when to use them, such as accuracy, precision, recall, F1 score, and ROC-AUC.

Example

“I evaluate model performance using metrics like accuracy for balanced datasets, but for imbalanced datasets, I prefer precision and recall. For instance, in a fraud detection model, I focus on recall to ensure we catch as many fraudulent cases as possible.”

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

This question gauges your knowledge of model generalization.

How to Answer

Mention techniques like cross-validation, regularization, and pruning.

Example

“To prevent overfitting, I use techniques such as cross-validation to ensure the model performs well on unseen data. Additionally, I apply regularization methods like L1 and L2 to penalize overly complex models.”

Data Structures and Algorithms

1. Can you explain the concept of a binary tree and its traversal methods?

This question tests your understanding of fundamental data structures.

How to Answer

Define a binary tree and describe its traversal methods, including in-order, pre-order, and post-order.

Example

“A binary tree is a data structure where each node has at most two children. The traversal methods include in-order, where we visit the left subtree, the node, and then the right subtree; pre-order, where we visit the node first; and post-order, where we visit the node last.”

2. How would you implement a queue using stacks?

This question assesses your problem-solving skills and understanding of data structures.

How to Answer

Explain the logic behind using two stacks to implement a queue and provide a brief overview of the operations.

Example

“I would use two stacks: one for enqueueing elements and another for dequeueing. When dequeuing, if the second stack is empty, I would pop all elements from the first stack and push them onto the second stack, effectively reversing the order to maintain queue behavior.”

3. What is the time complexity of searching in a balanced binary search tree?

This question tests your knowledge of algorithm efficiency.

How to Answer

Discuss the properties of balanced binary search trees and their impact on search operations.

Example

“In a balanced binary search tree, the time complexity for searching is O(log n) because the tree maintains a balanced height, allowing us to eliminate half of the remaining nodes at each step.”

4. Can you explain the sliding window technique and provide an example of its application?

This question evaluates your understanding of algorithmic techniques.

How to Answer

Define the sliding window technique and describe a scenario where it can be applied.

Example

“The sliding window technique is used to solve problems involving contiguous subarrays. For example, to find the maximum sum of any subarray of size k, I would maintain a window of size k, updating the sum as the window slides through the array.”

Behavioral Questions

1. Describe a time when you had to work with a difficult team member. How did you handle it?

This question assesses your interpersonal skills and conflict resolution abilities.

How to Answer

Use the STAR method (Situation, Task, Action, Result) to structure your response.

Example

“In a previous project, I worked with a team member who was resistant to feedback. I scheduled a one-on-one meeting to discuss our goals and the importance of collaboration. By actively listening to their concerns, we found common ground, which improved our teamwork and project outcomes.”

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

This question evaluates your time management skills.

How to Answer

Discuss your approach to prioritization, such as using a task management system or assessing project deadlines.

Example

“I prioritize tasks based on deadlines and project impact. I use tools like Trello to visualize my workload and ensure I allocate time effectively, focusing on high-impact tasks first while keeping track of upcoming deadlines.”

3. Why do you want to work at BNY Mellon?

This question gauges your interest in the company and role.

How to Answer

Discuss your alignment with the company’s values and how the role fits your career goals.

Example

“I admire BNY Mellon’s commitment to innovation in financial services. I am excited about the opportunity to leverage my machine learning skills to contribute to data-driven decision-making and enhance customer experiences.”

4. Can you give an example of a time you had to learn a new technology quickly?

This question assesses your adaptability and willingness to learn.

How to Answer

Provide a specific example of a technology you learned and how you applied it.

Example

“When I needed to implement a machine learning model using TensorFlow, I dedicated a weekend to complete an online course. I quickly applied what I learned to a project, which resulted in a successful deployment of the model within a tight deadline.”