Preparing for an interview at RBC requires a structured approach that balances technical mastery with business acumen. You should focus your preparation on four core evaluation pillars that the hiring team uses to assess candidates.

Role-Related Knowledge – You must demonstrate a deep understanding of core machine learning algorithms, statistical modeling, and data manipulation. Be ready to explain the "why" behind your technical choices, such as why you chose a specific loss function or evaluation metric for a past project.

Problem-Solving Ability – Interviewers want to see how you structure ambiguous problems. When presented with a business scenario, break down your approach systematically: define the objective, identify the required data sources, outline your modeling strategy, and explain how you would validate and deploy the solution.

Communication & Stakeholder Management – As a Data Scientist, you will regularly interact with non-technical partners. Practice translating complex mathematical concepts into clear, actionable business insights. Use the STAR (Situation, Task, Action, Result) framework to structure your behavioral answers, emphasizing the tangible business outcomes of your work.

Culture Fit & Regulatory Awareness – RBC operates in a highly regulated industry. Showing an appreciation for data governance, model explainability, ethical AI practices, and risk management will set you apart from other candidates.

The interview process for a Data Scientist at RBC is designed to evaluate both your technical execution and your collaborative fit. While the exact steps can vary slightly depending on the specific team (such as retail banking, wealth management, or quantitative risk), the overall progression remains consistent and structured.

The journey typically begins with an initial screening, which may be a brief call with a recruiter or a direct invitation to an online technical assessment. For many technical roles, RBC utilizes proctored coding assessments, such as a CodeSignal test, to evaluate your algorithmic problem-solving skills early in the process. Alternatively, some teams may send a take-home data science assignment where you are tasked with building and evaluating a machine learning model using a sample dataset.

Following the initial screen, you will move into the technical interview rounds. These sessions are conducted by senior data scientists, team leads, or managers. They will dive deep into your resume, ask detailed questions about your past projects, and test your knowledge of SQL, Python, machine learning algorithms, and database fundamentals. The final stage typically involves a conversation with a director or hiring manager, focusing on team fit, business alignment, and your long-term career aspirations within the bank.

The timeline shown above outlines the standard progression from your initial application to the final decision. Candidates should expect the entire process to take between three to six weeks, depending on team availability and the specific requirements of the business unit. Use this timeline to pace your study plan, ensuring you are fully prepared for the rigorous technical assessments before advancing to the final panel rounds.

To succeed in the RBC interview process, you must understand the specific technical and behavioral dimensions on which you will be graded. The evaluation is divided into several key areas, each testing a different aspect of your data science toolkit.

Classical Machine Learning & Time Series

This area evaluates your foundational knowledge of machine learning. Because RBC is a bank, models must comply with strict internal and external audit standards. Consequently, you will be heavily tested on interpretable, classical models rather than complex, unexplainable deep learning architectures.

Be ready to go over:

• Linear and Logistic Regression – Understand their mathematical formulations, assumptions, and diagnostic tests (e.g., multicollinearity, heteroscedasticity).
• Time Series Forecasting – Master autoregressive models (ARIMA, SARIMAX), smoothing techniques, and how to handle seasonality and trends in financial data.
• Model Evaluation – Know when to use precision, recall, F1-score, ROC-AUC, and PR-AUC, especially in the context of highly imbalanced datasets.
• Advanced concepts (less common) – Tree-based ensemble methods (Random Forests, Gradient Boosting), clustering algorithms (K-Means, hierarchical clustering), and dimensionality reduction (PCA).

Example questions or scenarios:

• "How would you design a model to predict customer churn using six months of transaction and login history?"
• "If your time series model is underperforming due to sudden market shifts, how would you adjust your feature engineering or training window?"

SQL, Databases & Data Manipulation

Data scientists at RBC spend a significant portion of their time querying, cleaning, and preparing data from massive, distributed databases. This evaluation area tests your ability to write efficient SQL queries and manipulate data programmatically.

Be ready to go over:

• Query Optimization – Understanding how to write efficient queries on tables with billions of rows, including the use of indexes and execution plans.
• Data Aggregation and Windowing – Mastering complex joins, subqueries, common table expressions (CTEs), and analytical window functions.
• Data Cleaning in Python – Leveraging libraries like Pandas and NumPy to handle missing values, outliers, and data type conversions.
• Advanced concepts (less common) – ETL pipeline design, database normalization vs. denormalization, and working with NoSQL databases.

Example questions or scenarios:

• "Write a SQL query using a window function to identify the top 5% of transacting customers in each branch over the last quarter."
• "How would you handle a situation where 30% of your target variable's history is missing due to a legacy system migration?"

Algorithmic Coding (CodeSignal)

For many entry-level, graduate, and intermediate roles, RBC uses a proctored online coding assessment to filter candidates. This test evaluates your raw coding speed, accuracy, and algorithmic efficiency.

Be ready to go over:

• Data Structures – Proficient use of arrays, hash maps, strings, and trees.
• Algorithmic Strategies – Two-pointer techniques, sliding windows, sorting, and searching.
• Time and Space Complexity – Being able to analyze and optimize your code to run within strict time limits (Big O notation).
• Advanced concepts (less common) – Dynamic programming, graph traversal (BFS/DFS), and bit manipulation.

Example questions or scenarios:

• "Implement a function that takes a list of stock prices and returns the maximum profit you could achieve by buying and selling once."
• "Given an array of transaction descriptions, write a parser to extract and group transaction amounts by merchant category."

Behavioral & Stakeholder Management

This area assesses your soft skills, communication style, and alignment with RBC's collaborative culture. You must demonstrate that you can work effectively within a team and manage expectations under pressure.

Be ready to go over:

• Project Walkthroughs – Explaining your past work clearly, focusing on your individual contribution and the business value generated.
• Handling Ambiguity – Describing how you proceed when project requirements are vague or data is incomplete.
• Conflict Resolution – Discussing how you handle disagreements with team members or impatient stakeholders.
• Advanced concepts (less common) – Navigating regulatory constraints, ethical data usage, and model governance policies.

Example questions or scenarios:

• "Tell me about a time you had to deliver bad news to a stakeholder—for example, that a highly anticipated model was not performing well enough to deploy."
• "Describe a situation where you had to collaborate with a data engineering team to resolve a bottleneck in your model's data pipeline."