As a Data Scientist at BNY, you sit at the unique intersection of cutting-edge technology and global financial services. BNY is a premier financial institution responsible for safeguarding and managing trillions of dollars in assets. This unprecedented scale means that the data science team is tasked with solving incredibly complex, high-impact problems. From optimizing asset management portfolios and predicting market trends to building advanced fraud detection systems and integrating generative AI into legacy pipelines, your work directly influences the stability and efficiency of the global financial ecosystem.

The role of a Data Scientist here goes far beyond traditional statistical analysis. You will be building, scaling, and deploying production-grade machine learning models that process massive, highly structured, and unstructured datasets. Whether you are joining as a generalist or stepping into a senior role such as a Vice President Data Scientist, you will be expected to drive innovation, design robust machine learning pipelines, and translate complex algorithmic outcomes into actionable business strategies for executive stakeholders.

What makes this role particularly exciting is BNY's active commitment to modernizing its technology stack. The data science division is heavily focused on the latest trends, including Large Language Models (LLMs), deep learning, and advanced neural networks. By joining this team, you will have the opportunity to work on highly sophisticated financial products, collaborate with world-class engineers, and leverage substantial computing resources to deliver measurable business impact.

The questions you will encounter during the BNY hiring process are designed to test your technical execution, theoretical understanding, and behavioral alignment. These questions are drawn from real interview experiences across multiple global offices and represent what you are highly likely to face.

Machine Learning & Deep Learning Concepts

These questions assess your foundational knowledge of machine learning, deep learning, and modern AI architectures, with a specific focus on modern trends like Large Language Models.

• Explain the architecture of a transformer model and how self-attention mechanisms function.
• How do you address overfitting when training a deep neural network on a relatively small dataset?

This list of questions represents the core technical and behavioral competencies evaluated at BNY. Candidates should use these examples to practice structuring their technical explanations and formatting their behavioral responses using the STAR method (Situation, Task, Action, Result). Pay special attention to explaining the business impact of your technical decisions.

Preparing for a Data Scientist interview at BNY requires a balanced approach that covers core programming, machine learning theory, system design, and behavioral preparation. You should approach your preparation systematically, ensuring you can write production-ready code while also being able to explain the high-level business value of your models.

Technical Excellence – You must demonstrate strong proficiency in Python, SQL, and core machine learning frameworks. This includes the ability to write clean, optimized code on the spot, design database queries, and build neural networks.

Systemic & Design Thinking – Especially for senior roles like Vice President Data Scientist, you need to demonstrate that you can think beyond the model itself. Interviewers will evaluate your ability to design scalable pipelines, handle data drift, and deploy models securely within a regulated financial framework.

Business Acumen & Communication – BNY values data scientists who can bridge the gap between complex mathematics and business operations. You must be able to clearly communicate why a model matters, how it impacts the bottom line, and how you validated its fairness and accuracy.

Collaboration & Leadership – You will be working in highly cross-functional environments alongside product managers, software engineers, and compliance officers. Showing that you can collaborate effectively, take constructive feedback, and lead initiatives is critical to your success.

The interview process for a Data Scientist at BNY is structured to thoroughly evaluate both your technical capabilities and your behavioral alignment with the firm's culture. While the exact steps can vary slightly depending on the seniority of the role and the specific team, the overall process is designed to be rigorous, transparent, and comprehensive.

The journey typically begins with an online technical assessment. This initial screen is highly technical and tests your coding speed, SQL knowledge, and theoretical machine learning concepts. Following a successful assessment, you will transition to structured technical rounds, which are often conducted in partnership with external technical evaluation platforms. These rounds focus heavily on practical coding, system design, and algorithmic problem-solving.

The final stages of the process bring you face-to-face with the hiring team and senior leadership. During these onsite or virtual panel interviews, the focus shifts toward system design, behavioral attributes, and your past project experiences. BNY places a strong emphasis on finding candidates who not only possess deep technical expertise but also exhibit strong communication skills and a collaborative mindset.

This visual timeline outlines the typical progression a candidate goes through from the initial application to the final offer. Candidates should expect the entire process to take anywhere from three to six weeks, depending on scheduling and team availability. Use this timeline to pace your preparation, ensuring you are fully ready for the deep-dive technical rounds before advancing past the initial screens.

To succeed in the BNY interview process, you must understand exactly what is expected of you in each key evaluation area. The interviewers use specific rubrics to score your performance across coding, machine learning theory, and system design.

Online Technical Assessments

The online assessment (OT) is your first opportunity to showcase your technical skills. This round is designed to filter for core competency in programming and database management.

Be ready to go over:

• Jupyter Notebook Environments – You may be asked to build, train, and evaluate a machine learning model on a provided dataset within an active Jupyter notebook interface.
• SQL Proficiency – Expect intermediate to advanced SQL questions focusing on joins, window functions, and complex aggregations.
• Python Libraries – Be comfortable using pandas, numpy, and scikit-learn to manipulate datasets, handle missing data, and extract features.
• Advanced concepts (less common) – Writing custom loss functions, training neural networks from scratch under tight time constraints, and optimizing model training loops.

Example scenarios:

• "You are given a dataset of historical transaction data in a Jupyter notebook. Clean the data, engineer three relevant features, train a random forest model, and output the classification report within 45 minutes."
• "Write a SQL query to identify the top 5% of customers based on their transaction volume over a rolling 30-day window."

Technical Screening (Karat)

The technical screen is a live, interactive coding and theory session. It is designed to test how you think and write code under pressure, as well as your understanding of core data structures and algorithms.

Be ready to go over:

• Data Structures & Algorithms – Standard software engineering questions focusing on arrays, strings, hash maps, and search algorithms.
• Machine Learning Short Answers – Rapid-fire questions testing your understanding of bias-variance tradeoffs, regularization, and optimization algorithms.
• SQL Live Coding – Live-coding a SQL query based on a schema presented to you during the call.
• Advanced concepts (less common) – Deep learning architectures, backpropagation mathematics, and optimization techniques like Adam or RMSProp.

Example scenarios:

• "Given a stream of real-time stock prices, write a Python function to find the moving average over a sliding window of size K."
• "Explain the mathematical difference between L1 and L2 regularization and how they affect model weights."

ML System Design & Case Studies

This round evaluates your ability to architect end-to-end machine learning systems. Interviewers want to see how you handle ambiguity, scale, and deployment constraints.

Be ready to go over:

• End-to-End Pipelines – Designing systems from data ingestion and feature stores to model training, deployment, and monitoring.
• Generative AI & LLMs – Integrating LLMs, designing RAG pipelines, and handling prompt engineering and vector databases.
• Model Monitoring & Maintenance – Strategies for detecting data drift, concept drift, and setting up automated retraining loops.
• Advanced concepts (less common) – Designing distributed training systems for massive datasets, managing model latency in high-frequency trading environments, and federated learning.

Example scenarios:

• "Design an enterprise-level search assistant using Retrieval-Augmented Generation (RAG) that allows analysts to query internal financial documents securely."
• "How would you design a system to detect anomalous trading patterns across millions of daily transactions, ensuring false positives are minimized?"