Preparation for the ASML interview process requires a balanced focus on technical aptitude and behavioral alignment. You should approach this process ready to demonstrate not just your coding skills, but your ability to apply those skills to physical and business processes.

Key Evaluation Criteria

Technical Proficiency & Theoretical Depth – 2–3 sentences describing: You must demonstrate a strong grasp of machine learning foundations (regression, classification, clustering) and the mathematical principles behind them. Interviewers will evaluate your ability to select the right model for a given problem and your proficiency in Python (pandas, scikit-learn) and SQL for data manipulation.

Domain Application & Problem Solving – 2–3 sentences describing: ASML values candidates who can translate abstract data problems into concrete business or engineering solutions. You will be assessed on your ability to structure ambiguous problems, particularly those related to manufacturing, supply chain, or operational efficiency, and drive them toward a measurable outcome.

Collaboration & Communication – 2–3 sentences describing: Given the complex, cross-functional nature of our work, you must effectively communicate technical findings to non-technical stakeholders and upper management. Evaluation focuses on how you document your work, visualize data (using tools like Tableau or Power BI), and advocate for your recommendations.

Cultural Fit (The "ASML Way") – 2–3 sentences describing: We look for the "Challenge, Collaborate, Care" mindset. Interviewers will assess your ability to take ownership of results, navigate a demanding environment with changing workloads, and work independently while remaining an integral part of a supportive team.

The interview process for a Data Scientist at ASML is designed to be thorough yet efficient, focusing on identifying candidates who possess both the technical rigor and the collaborative spirit required for our teams. Generally, the process begins with a recruiter screening to verify your background, interest in the semiconductor industry, and eligibility (including export control requirements). This is followed by a technical screening, which may involve a discussion with a hiring manager or a senior data scientist to gauge your experience with specific tools like Python, SQL, and ML frameworks.

Successful candidates proceed to a series of interviews, often conducted as a panel or back-to-back sessions. These rounds are split between deep technical dives—where you may be asked to walk through previous projects, discuss model architecture, or solve a case study—and behavioral interviews focused on soft skills. ASML places significant weight on your past projects, so expect detailed questions about the "why" and "how" of your portfolio. The process is distinctive in its emphasis on practical application; we are less interested in rote memorization and more interested in how you handle data in real-world, often messy, scenarios.

This timeline illustrates the typical progression from your initial application to the final offer stage. You should use this overview to pace your preparation, ensuring you have refreshed your technical concepts before the screening and prepared your behavioral stories (STAR method) before the final rounds. Note that specific steps may vary slightly depending on whether the role is within the BI&A team or Customer Support operations.

The evaluation for Data Scientists at ASML focuses heavily on your ability to deploy machine learning in a business or engineering context. You must show that you can handle the end-to-end data lifecycle, from extraction to model deployment and visualization.

Machine Learning & Modeling

This is the core of the technical assessment. You need to demonstrate that you understand not just how to implement a model, but the mathematical theory behind it. Interviewers want to know why you chose a specific algorithm and how you validate its performance.

Be ready to go over:

• Supervised Learning – Regression (linear, logistic) and classification techniques. Know the assumptions and limitations of each.
• Unsupervised Learning – Clustering (K-Means, Hierarchical) and dimensionality reduction (PCA), often used for anomaly detection in manufacturing data.
• Deep Learning – Familiarity with neural networks, specifically CNNs (often used for image data in lithography) and RNNs/LSTMs (for time-series data).
• Advanced concepts – Optimization techniques, hyperparameter tuning, and handling imbalanced datasets.

Example questions or scenarios:

• "Explain the difference between L1 and L2 regularization and when you would use each."
• "How would you approach a predictive maintenance problem where failure data is extremely rare?"
• "Describe a time you used a Random Forest classifier. How did you determine feature importance?"

Data Manipulation & Engineering

A model is only as good as the data feeding it. ASML evaluates your ability to "get your hands dirty" with data. You must be proficient in extracting data from various sources and cleaning it for analysis.

Be ready to go over:

• SQL – Complex joins, aggregations, window functions, and subqueries.
• Python Data Stack – Proficiency with Pandas and NumPy for data transformation.
• ETL Pipelines – Understanding how to design and contribute to end-to-end data pipelines.
• Data Quality – Techniques for handling missing values, outliers, and noise in sensor or business data.

Example questions or scenarios:

• "Write a SQL query to find the top 3 performing machines per region based on uptime."
• "How do you handle datasets that are too large to fit into memory?"
• "Describe your process for validating data integrity before training a model."

Visualization & Business Insight

Data Science at ASML ends with actionable insights. You will be evaluated on your ability to translate complex model outputs into clear reports and dashboards that stakeholders can understand.

Be ready to go over:

• Dashboarding Tools – Experience with Tableau, Spotfire, or Power BI.
• Storytelling – structuring a presentation to lead to a recommendation.
• KPI Definition – Identifying and documenting goals and targets (e.g., yield, throughput, cost savings).

Example questions or scenarios:

• "How would you visualize the performance of a machine learning model for a non-technical manager?"
• "Tell me about a time your data analysis led to a concrete change in business strategy."
• "Design a dashboard to monitor the health of a fleet of lithography systems."

As a Data Scientist at ASML, your daily work revolves around solving complex challenges that directly impact our ability to service customers and improve operations. You are responsible for the full lifecycle of data projects. This begins with exploring and importing data from multiple sources, ensuring it is "machine learning ready." You will spend a significant portion of your time cleaning data, performing feature engineering, and validating data quality to ensure robust results.

Once the data is prepared, you will develop, document, and deploy machine learning models to address specific business problems. This could range from optimizing planning operations to identifying bottlenecks in the fab. You will analyze results rigorously and interpret them to create clear reports and compelling data visualizations. Collaboration is key; you will work closely with engineers and department leads to implement your solutions and present your findings to upper management, driving innovation across the organization.