Introduction to the jobASML Customer Support (CS) Diagnostics is at the core of ASML’s ambition to significantly reduce diagnostic labor hours, improve system availability, and enable predictive and self‑healing service capabilities towards 2030.

The Data Engineering Engineer who will play a key role in building, scaling, and operationalizing AI‑driven diagnostics, observability, and predictive maintenance solutions.

This role goes beyond tooling or automation: you will own the full lifecycle of data and AI solutions that directly impact diagnostic accuracy, MTTR, MTBF, and service efficiency.
And you will work at the intersection of machine data, diagnostics domain knowledge, and advanced analytics, collaborating closely with CS Diagnostics, Field, D&E, and central platform teams
Role and responsibilities
AI, Analytics & Model Ownership
Design, develop, deploy, and maintain machine learning and deep learning models for Predictive Maintenance (PdM), Fault Detection & Classification, and root‑cause identification and observability improvement.

Own the end‑to‑end model lifecycle, problem definition and data exploration, feature engineering and model development, validation, deployment, monitoring, and retraining.

Continuously improve model performance based on field feedback, diagnostic outcomes, and new data availability.

Data Engineering & Platform DevelopmentDesign and implement scalable, cloud‑native data pipelines to ingest, transform, and provision large volumes of structured and unstructured machine data.

Work with platforms such as Azure, Databricks, Spark, and Kusto to ensure reliable, performant, and secure data access.

Ensure data quality, traceability, and reproducibility for downstream analytics and AI applications.

Enable early access to data through proof‑of‑concept pipelines, while ensuring smooth transition to production‑grade solutionsDiagnostics Domain EnablementImprove observability through machine data by identifying gaps, defining required signals, and translating diagnostic needs into data and model requirements.

Identify structural improvements in diagnostic services, including processes, methods, and analytical approaches.

Closely collaborate with diagnostics experts to ensure solutions are actionable, interpretable, and embedded in diagnostic workflows.

Standards, Governance & Stakeholder CollaborationDefine and follow standards, policies, and protocols for data, models, and analytics solutions.

Ensure solutions are compliant, manageable, scalable, and secure, in line with ASML requirements.

Translate technical outcomes into measurable service impact (e.g. MTTR reduction, labor hour savings, improved hit rates).

Communicate results, insights, and recommendations to senior stakeholders and leadership.

Provide guidance and knowledge sharing to colleagues and stakeholders where needed.

Education, Experience and SkillMaster’s degree in Data Science, Computer Science, Engineering, Applied Mathematics, or a related field.5+ years of relevant experience in data science, data engineering, or advanced analytics roles.

Strong proficiency in Python and experience with analytical and ML libraries. Scripting skill such as PERL, Bash, Power ShellProven experience developing and deploying machine learning / deep learning models in production environments.

Strong experience with cloud‑based data platforms (Azure preferred), including Databricks, Spark, SQL / Kusto. Experience with SQL ETL processes.

Solid understanding of statistics, data analysis, SPC/FDC concepts, and analytical problem solving.

Experience working with large‑scale, high‑frequency data streams

Other information

Preferred Qualifications

Experience with diagnostics, manufacturing, equipment data, or industrial systems.
Familiarity with ASML machine data and CS diagnostics workflows.
Experience improving observability, fault detection, or predictive maintenance in complex systems.
Experience working with business stakeholders and explaining technical results to non-technical audiences.
Experience training others and creating technical documentation or user manuals.
This position requires access to controlled technology, as defined in the United States Export Administration Regulations (15 C.F.R.730, et seq.). Qualified candidates must be legally authorized to access such controlled technology prior to beginning work.
Business demands may require ASML to proceed with candidates who are immediately eligible to access controlled technology.

Inclusion and diversityASML is an Equal Opportunity Employer that values and respects the importance of a diverse and inclusive workforce. It is the policy of the company to recruit, hire, train and promote persons in all job titles without regard to race, color, religion, sex, age, national origin, veteran status, disability, sexual orientation, or gender identity.

We recognize that inclusion and diversity is a driving force in the success of our company.

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The Data Engineering Engineer who will play a key role in building, scaling, and operationalizing AI‑driven diagnostics, observability, and predictive maintenance solutions.

This role goes beyond tooling or automation: you will own the full lifecycle of data and AI solutions that directly impact diagnostic accuracy, MTTR, MTBF, and service efficiency.
And you will work at the intersection of machine data, diagnostics domain knowledge, and advanced analytics, collaborating closely with CS Diagnostics, Field, D&E, and central platform teams
Role and responsibilities
AI, Analytics & Model Ownership
Design, develop, deploy, and maintain machine learning and deep learning models for Predictive Maintenance (PdM), Fault Detection & Classification, and root‑cause identification and observability improvement.