KONUX Data Scientist interview questions & guide 2026

The figures provided represent the current market compensation range for a Senior Data Scientist based in München. Candidates should view these as a baseline for total compensation, which may include additional benefits or equity components typical of high-growth technology companies in the region. When negotiating, consider your total years of experience, specific domain expertise in IoT or predictive analytics, and the seniority level of the specific team you are joining.

Our interview process is designed to evaluate your ability to solve real-world problems under pressure while ensuring you align with our collaborative, mission-driven culture. While specific questions may vary by team, the following categories represent the core competencies we test.

SQL and Data Manipulation

These questions test your ability to extract, clean, and manipulate data efficiently. You will be expected to demonstrate proficiency in handling complex datasets common in industrial settings.

• How would you use SQL window functions to calculate a rolling average of sensor noise over time?
• Describe your process for identifying and handling missing or corrupted data points in a high-frequency time-series dataset.

Preparation at KONUX should focus on your ability to articulate your methodology. We are less interested in memorized definitions and more interested in how you structure your thinking when faced with an open-ended problem.

Role-related knowledge – You must be fluent in the tools of the trade, specifically SQL, Python, and statistical modeling. Expect to apply these tools to scenarios involving time-series data and predictive maintenance.

Problem-solving ability – We will present you with ambiguous scenarios where the "right" answer is not immediately obvious. Focus on communicating your thought process clearly, stating your assumptions, and validating your approach.

Culture fit and values – KONUX is a fast-paced environment. We look for individuals who are proactive, comfortable with change, and capable of collaborating across diverse, cross-functional teams.

The KONUX interview process is designed to be efficient, respectful, and comprehensive. It typically begins with a recruiter screening, followed by a series of technical and cultural evaluations. We value your time and aim to provide a transparent experience that allows you to assess if we are the right fit for your career goals.

The process often includes a take-home coding assignment. This is a critical stage where we evaluate your ability to handle real-world data problems independently. Following this, you will present your findings to the team, which serves as a gateway to deeper technical and behavioral discussions.

This timeline illustrates the progression from initial screening to final decision. Candidates should use this as a roadmap to manage their preparation energy, ensuring they are well-rested for the final onsite or deep-dive technical rounds. Note that while the process is generally structured, we maintain some flexibility to accommodate candidate schedules where possible.

A/B Testing and Experimentation

We test your ability to design robust experiments that lead to actionable business decisions. A strong candidate understands the mathematical requirements for validity and the practical constraints of a real-world product.

• Be ready to go over:
• Statistical significance – Understanding p-values and confidence intervals.
• Experimentation pitfalls – Dealing with selection bias and network effects.

As a Data Scientist at KONUX, you will own the end-to-end data lifecycle. Your day-to-day will involve:

• Model Development: Building and deploying predictive models that analyze sensor streams to forecast infrastructure health.
• Cross-functional Collaboration: Working closely with product managers and software engineers to integrate your models into the KONUX product suite.
• Metric Ownership: Defining and monitoring the success metrics that guide our product development cycles.
• Data Infrastructure: Partnering with data engineers to ensure high-quality data pipelines are in place to support your analytical needs.

We seek candidates who combine deep technical expertise with a pragmatic, product-oriented mindset.

• Must-have skills:

• Advanced proficiency in SQL (including window functions) and Python.

• Solid foundation in A/B testing and inferential statistics.

• Ability to diagnose and solve complex technical problems in a time-sensitive environment.

• Experience working with high-volume or high-frequency datasets.

• Nice-to-have skills:

• Experience in the industrial IoT, railway, or predictive maintenance sectors.

• Familiarity with cloud-based machine learning deployment workflows.

Q: How long does the interview process take? A: We pride ourselves on being efficient. From the initial screening to the final decision, the process typically spans 4 to 8 weeks, depending on scheduling.

Q: How much should I prepare for the coding assignment? A: The assignment is representative of the actual work you will do. Treat it as a professional project—focus on clean, maintainable code and clear documentation of your methodology.

Q: Is the team culture collaborative or competitive? A: It is highly collaborative. We solve difficult problems that require input from multiple disciplines; we succeed or fail as a team.

• Structure your answers: Use the STAR method (Situation, Task, Action, Result) for behavioral questions to keep your responses concise and impactful.
• Be ready to discuss the "why": We value candidates who understand the business context behind their technical decisions.
• Ask thoughtful questions: Use the time at the end of your interviews to ask about our data challenges, team structure, or long-term vision. It shows you are engaged and serious about the role.
• Own your mistakes: If you get stuck on a technical problem, walk us through your thinking rather than staying silent. We want to see how you troubleshoot.

The Data Scientist role at KONUX is a high-impact position that offers the chance to apply advanced analytics to real-world industrial infrastructure. By focusing your preparation on SQL proficiency, A/B testing methodology, and clear communication of your problem-solving process, you will be well-positioned to succeed in our evaluation loop.

We encourage you to visit Dataford to explore additional interview insights, practice questions, and strategic preparation resources tailored to this role. You possess the skills to thrive in this environment; stay focused on your strengths, articulate your process clearly, and approach each stage of the process as an opportunity to demonstrate your unique value to the KONUX team.