BNY Data Scientist interview questions & guide 2026

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?"

As a Data Scientist at BNY, your day-to-day responsibilities will vary depending on your team, but they generally center around turning vast amounts of financial data into strategic advantages. You will be responsible for the entire model lifecycle, from ideation to production.

• Model Development & Training – You will design, build, and train machine learning and deep learning models to solve complex business problems, such as risk forecasting, fraud detection, and operational automation.
• Data Engineering & Pipeline Construction – You will work closely with data engineers to build robust, scalable data pipelines, ensuring that your models have access to high-quality, real-time data.
• GenAI Innovation – You will explore, prototype, and deploy generative AI and LLM-based solutions to improve internal workflows and enhance client-facing products.
• Stakeholder Collaboration – You will collaborate with product managers, software engineers, and business leaders to define project requirements, share model insights, and ensure successful deployment.
• Leadership & Mentorship – If you are operating at the Vice President Data Scientist level, you will also be expected to mentor junior team members, drive the technical roadmap, and represent the data science team in high-level executive meetings.

To be competitive for a Data Scientist position at BNY, you must possess a strong blend of technical expertise, academic foundation, and professional experience.

Technical Skills

• Programming Languages – Expert-level proficiency in Python is mandatory, along with a strong command of SQL for data retrieval.
• Machine Learning Frameworks – Deep experience with libraries such as scikit-learn, PyTorch, TensorFlow, XGBoost, and Hugging Face.
• Data Manipulation – Mastery of pandas, numpy, and data visualization tools like matplotlib or seaborn.
• Modern AI Technologies – Solid understanding of LLMs, vector databases (e.g., Pinecone, Milvus), and RAG architectures.

Experience & Soft Skills

• Professional Background – A degree in a quantitative field (Computer Science, Statistics, Mathematics, Engineering) is highly preferred, along with several years of hands-on industry experience.
• Domain Knowledge – While not always mandatory, prior experience in financial services, fintech, quantitative trading, or risk management is highly advantageous.
• Communication Skills – The ability to articulate complex mathematical concepts to non-technical stakeholders clearly and confidently.

Must-Have vs. Nice-to-Have

• Must-Have – Strong Python coding skills, intermediate-to-advanced SQL, solid theoretical understanding of machine learning algorithms, and a proven track record of deploying models.
• Nice-to-Have – Experience with cloud platforms (AWS, Azure, GCP), familiarity with containerization (Docker, Kubernetes), experience building GenAI applications in production, and prior leadership or mentorship experience.

Q: How difficult is the BNY Data Scientist interview?

A: The interview process is generally rated as average to difficult. The initial online assessments and technical screens are highly rigorous and require strong coding speed and theoretical knowledge. However, the onsite rounds focus heavily on practical application, system design, and behavioral alignment, making thorough preparation highly effective.

Q: What is the role of Karat in the BNY interview process?

A: BNY frequently partners with Karat to conduct their first-round technical interviews. These are structured, live-coding sessions led by experienced technical interviewers. They focus on data structures, algorithms, SQL, and short-answer machine learning theory.

Q: Does BNY focus heavily on Generative AI and LLMs?

A: Yes. BNY is actively investing in modernizing its technology stack and places a strong emphasis on GenAI, LLMs, and modern deep learning trends. Even if the specific role is not exclusively focused on NLP, showing familiarity with these technologies will significantly strengthen your candidacy.

Q: What is the typical timeline from application to offer?

A: The timeline can vary, but most candidates report a process lasting between three to six weeks. BNY is known for having a highly structured process, though scheduling delays can occasionally occur between rounds. Proactive communication with your recruiter is highly recommended.

Q: What is the hybrid/remote work policy at BNY?

A: BNY generally operates under a hybrid model, requiring employees to be in the office a set number of days per week (typically three days). This varies by location (such as Pittsburgh, PA, Lake Mary, FL, or New York, NY) and specific team agreements.

To maximize your chances of success during the BNY Data Scientist interview process, keep these practical, insider tips in mind.

• Master the Jupyter Environment: For the online test, practice building and training a model from scratch within a Jupyter notebook under a strict time limit. Focus on clean code, proper validation splits, and clear evaluation metrics.
• Brush Up on SQL: Do not underestimate the SQL portion of the interviews. Practice complex joins, window functions, and subqueries, as you will face them in both the online test and live technical screens.
• Structure Your Case Studies: When answering ML system design questions, use a structured framework. Start with the business objective, move to data ingestion and exploration, then model selection, evaluation, deployment, and finally monitoring.
• Be Prepared for GenAI Questions: Even if your primary background is in classical machine learning, review the fundamentals of transformers, embeddings, and vector databases. This knowledge is highly valued across all data science teams at BNY.

• Showcase Your Business Impact: When discussing your past projects, always highlight the business outcome. Use metrics like revenue generated, costs saved, latency reduced, or accuracy improved to ground your technical achievements in real-world value.

Securing a Data Scientist or Vice President Data Scientist role at BNY is an incredible opportunity to apply cutting-edge machine learning and AI methodologies to some of the most complex financial challenges in the world. The role offers a unique combination of massive scale, rich datasets, and a strong organizational commitment to technological innovation. By preparing thoroughly for the technical rigor of the online assessments, mastering ML system design principles, and aligning your behavioral stories with the company's collaborative culture, you can position yourself as a standout candidate.

As you embark on your preparation journey, focus on building a deep, fundamental understanding of both classical machine learning and modern generative AI architectures. Practice coding efficiently, structuring your system designs logically, and communicating your ideas with confidence and clarity.

The compensation for a Data Scientist at BNY is highly competitive and reflective of the critical impact the role has on the organization. The package typically includes a strong base salary, performance-based bonuses, and a comprehensive benefits package. Senior roles like Vice President Data Scientist command higher salary bands and significant equity components, making it a highly rewarding career path.

For more detailed interview insights, real-world candidate experiences, and interactive preparation resources tailored specifically for BNY and other top-tier institutions, explore the comprehensive tools available on Dataford. With targeted practice and focused preparation, you have everything you need to succeed in your upcoming interviews. Good luck!