Comarch Data Scientist interview questions & guide 2026

Our interview process is designed to evaluate both your foundational technical knowledge and your ability to apply those concepts to real-world scenarios. The questions below reflect patterns observed in our hiring process and are intended to help you understand the breadth of topics we prioritize.

SQL and Data Manipulation

These questions assess your ability to handle data efficiently, a critical skill given our reliance on robust database systems.

• Write a query using SQL window functions to calculate a moving average of user activity.
• How would you optimize a slow-running SQL query involving multiple joins?

Preparation for a Data Scientist role at Comarch should be structured around both deep technical mastery and practical application. Do not simply memorize definitions; focus on explaining the "why" behind your technical choices.

Role-related knowledge – You must possess a strong grasp of core machine learning algorithms and database design. Be prepared to explain the mathematical intuition behind models and the practical trade-offs of different SQL approaches.

Problem-solving ability – We look for candidates who can structure ambiguous problems into logical, solvable components. When presented with a case study or technical question, talk through your thought process out loud to demonstrate your analytical framework.

Leadership and communication – Even in highly technical roles, the ability to translate data into business value is paramount. You will be evaluated on your ability to collaborate with others and articulate your ideas clearly, even under pressure.

Cultural alignment – We value candidates who are proactive, resilient, and collaborative. Be prepared to discuss how you contribute to a team environment and how you handle challenging or high-pressure situations.

The interview process at Comarch is designed to be rigorous but efficient. While the number of stages can vary depending on the team and seniority, you should expect a blend of technical assessments and behavioral discussions. The process often begins with a recruiter screening to discuss your background and interest, followed by one or more technical rounds.

You may encounter white-board coding sessions, deep-dive discussions on your past projects, or live problem-solving exercises. We value candidates who show consistency throughout the process, regardless of the interviewer or the specific technical focus. Our goal is to assess your practical skills in a professional, albeit challenging, environment.

The visual timeline above illustrates the typical progression from initial screening to technical evaluation. Use this to pace your preparation, ensuring you have enough time to brush up on both your coding fundamentals and your conceptual understanding of data science.

Technical Depth and Fundamentals

We assess your understanding of core algorithms and their implementation. A strong candidate moves beyond basic definitions to explain the mechanics of how models converge and the criteria for model selection.

Be ready to go over:

• Gradient descent and loss functions.
• Random Forest and ensemble methods.

As a Data Scientist, your primary responsibility is to transform raw data into insights that improve Comarch’s software solutions. This involves collaborating closely with product managers to define success metrics, working with engineers to ensure data quality, and developing models that can be deployed into production environments.

You will be expected to own the end-to-end data lifecycle for your projects. This includes everything from initial data exploration and hypothesis generation to model deployment and post-launch performance monitoring. You will also play a role in democratizing data across the team, ensuring that your colleagues can make informed decisions based on the insights you provide.

A competitive candidate for the Data Scientist position will demonstrate a blend of academic rigor and practical engineering experience.

• Must-have skills: Proficiency in SQL (including complex joins and window functions), strong knowledge of machine learning algorithms (Random Forest, Neural Networks), and the ability to explain statistical concepts clearly.
• Nice-to-have skills: Experience with cloud-based data stacks, familiarity with product analytics tools, and a track record of deploying models into production environments.
• Soft skills: Clear communication, stakeholder management, and the ability to thrive in a fast-paced, collaborative environment.

Q: How long does the hiring process typically take? A: The process can vary, but generally, it spans from a few weeks to a month. We aim to be transparent about the timeline once you enter the interview loop.

Q: Is the technical interview focused on theory or practical application? A: It is a mix of both. You should be prepared to discuss the theoretical underpinnings of your work while also demonstrating your ability to write code or design solutions on the spot.

Q: What is the best way to prepare for the behavioral rounds? A: Use the STAR method (Situation, Task, Action, Result) to structure your answers. Focus on specific examples where you demonstrated leadership, overcame a technical hurdle, or influenced a team decision.

Q: How can I stand out during the interview? A: Candidates who stand out are those who ask insightful questions about the business impact of their work and show a genuine curiosity for how Comarch products function at scale.

• Structure your thoughts: Whether answering a coding challenge or a product case study, always take a moment to outline your approach before diving into the details.
• Be honest about your limits: If you encounter a question on a topic you are less familiar with, explain your thought process and how you would go about finding the answer rather than guessing.
• Prepare for the environment: Be ready to code or solve problems on a whiteboard or a shared document. Practice writing clean, readable code in a simulated environment.
• Know your resume: Be prepared to dive into the technical details of any project you list on your resume, including why you chose a specific model or approach.

The Data Scientist role at Comarch is a challenging, high-visibility position that offers the opportunity to drive real-world impact. By mastering the fundamentals of SQL, A/B testing, and machine learning, you will be well-positioned to succeed in our technical evaluations. Remember that we are looking for engineers who are as interested in the business outcomes of their models as they are in the underlying mathematics.

For further practice, you can explore additional interview insights, practice questions, and preparation resources on Dataford. We encourage you to review these materials to build confidence and refine your approach. You have the skills to succeed; with focused, strategic preparation, you will be well-prepared to demonstrate your value to our team.

The module above provides insights into compensation expectations for the Data Scientist role. Candidates should interpret these figures as a range that accounts for varying levels of seniority, local market conditions, and specific team requirements. Use this data to calibrate your expectations during the offer phase.