ING Data Scientist interview questions & guide 2026

The following questions are representative of the patterns identified in real ING interview loops. While the specific technical focus may shift depending on the hiring team, you should prepare for a rigorous assessment of your analytical depth and communication skills.

Technical / SQL & Data Manipulation

These questions test your ability to handle large datasets and derive insights using standard industry tools.

• How would you use SQL window functions to calculate a rolling average of customer transaction volumes over the last 30 days?
• Given two tables (users and transactions), write a query to identify the top 3 spending categories for users who have been active in the last quarter.

Preparation at ING requires a balance of technical fluency and a "business-first" mindset. You are not just being evaluated on code; you are being evaluated on your ability to deliver value.

Role-related knowledge – You must be comfortable with Python, SQL, and common ML libraries. Interviewers will look for your ability to apply these tools to real-world financial problems rather than just knowing the syntax.

Problem-solving ability – This is the core of the case study and technical rounds. You should practice verbalizing your thought process as you tackle ambiguous problems, focusing on how you structure your logic before diving into the solution.

Communication & Stakeholder Management – ING values the ability to bridge the gap between technical data science and business strategy. Be prepared to explain your technical decisions in simple, clear language.

Ownership & Culture – As an Agile organization, ING values proactive team members who take responsibility for their work. Use the STAR method (Situation, Task, Action, Result) to frame your behavioral answers, highlighting your personal contribution and impact.

The interview process at ING is generally structured to assess your technical foundation, your problem-solving capacity, and your ability to fit into their collaborative, Agile culture. While the exact sequence can vary by location and team, you should expect a blend of automated assessments and live, interactive sessions.

The process typically begins with an initial screening, followed by a technical assessment that covers SQL and Python. Successful candidates move into a series of technical interviews, which may include a take-home assignment or a live case study presentation. The final stages usually involve discussions with the hiring manager and product owners to gauge team fit and long-term potential.

The timeline above represents a typical progression from application to final offer. Candidates should interpret this as a multi-stage funnel where each round builds upon the last; maintain consistent energy and focus from the initial assessment through to the final cultural interview.

Technical Rigor in ML and Data

This area is assessed through the technical assessment and the take-home assignment. You will be judged on your coding quality, your choice of models, and your ability to defend those choices.

Be ready to go over:

• Supervised learning workflows (feature engineering, model selection, validation).
• Distributed computing concepts (Spark, Hive) when dealing with large-scale data.

As a Data Scientist at ING, your day-to-day will be centered on the end-to-end lifecycle of AI/ML products. You will not just be building models; you will be acting as a project manager for your own solutions, ensuring they meet the high standards of the bank.

• Proactive Delivery: You will take ownership of the design and delivery of models, ranging from credit risk and income estimation to NLP solutions.
• Cross-functional Collaboration: You will work closely with business stakeholders, data analysts, and software engineers to co-create solutions.
• Engineering Excellence: You are expected to adhere to high standards of coding, utilizing Git and participating in peer reviews to ensure robustness.
• Value-driven Focus: Every project must have a clear business objective. You will be expected to demonstrate how your technical work contributes to the bank's goal of helping clients create a better future.

To be competitive, you should demonstrate a strong technical toolkit combined with a collaborative spirit.

• Must-have skills:
  • Proficiency in SQL and Python.
  • Proven experience in data analysis, visualization, and predictive modeling.
  • A degree in Mathematics, Statistics, Computer Science, or a related quantitative field.
  • Experience in supervised learning and working with large datasets.
• Nice-to-have skills:
  • Experience with distributed computing frameworks like Spark or Hadoop.
  • Familiarity with Agile methodologies.
  • Experience in a financial or highly regulated environment.

Q: How difficult are the technical assessments? A: They are designed to be challenging but fair. They test your practical application of SQL and Python; focus on writing clean, efficient code and showing your logical approach to data manipulation.

Q: What is the best way to prepare for the case study presentation? A: The presentation is as much about your communication as it is about the code. Focus on the "why" behind your decisions and how your solution impacts the business, rather than just listing the ML models you used.

Q: How much time should I spend preparing? A: Given the technical nature of the role, we recommend dedicating significant time to reviewing window functions, statistical testing principles, and your own past projects to ensure you can discuss them in depth.

Q: Does ING support remote work? A: ING embraces a flexible model that balances remote work with office-based collaboration. The specific balance is typically determined at the team level based on business needs.

• Prioritize clarity: When answering technical questions, always state your assumptions. If a question feels ambiguous, ask clarifying questions before jumping into a solution.
• Think in business terms: When discussing models, always link back to the business impact (e.g., "This model will help us reduce churn by X%").
• Know your code: If you submit a take-home assignment, be prepared to answer deep-dive questions about every line you wrote.
• Embrace Agile: Familiarize yourself with the Agile mindset. Understanding how to work in sprints and collaborate within a cross-functional team is a major plus.

The Data Scientist role at ING is a high-impact position that requires a blend of technical expertise and commercial awareness. By mastering SQL window functions, A/B testing frameworks, and the ability to diagnose metric drops, you will be well-positioned to succeed in the interview process.

Focus your preparation on demonstrating how you translate complex data into actionable business value. You can explore additional interview insights, practice questions, and preparation resources on Dataford to further sharpen your readiness.

The compensation data above provides a broad range for the role. Candidates should interpret these figures as market-standard indicators that vary based on experience, seniority, and specific location requirements. Use this information to benchmark your expectations while focusing your energy on showcasing your unique value proposition.