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# Data Scientist

- **Company:** YER
- **Location:** AMSTERDAM ZUIDOOST, Netherlands
- **Experience:** Expert
- **Contract:** Contract
- **Skills:** Training Data, 3d Models, Artificial Intelligence, Data Analysis, Data Cleansing, Extract Transform Load (ETL), Model Validation, Data Analytics
- **Published:** October 29, 2026
- **Apply:** https://www.yer.nl/solliciteren/?vacature-id=22105

## About the Role

Experience with data science and machine learning in an Azure environment, Databricks and Python toolkits is relevant to the role. Agentic AI skills are a plus., * A Master's degree or PhD in Data Science, Applied Mathematics, Applied Physics, Statistics, Machine Learning, Signal Processing, Operations Research or a related field.

 * Professional experience delivering analytics and machine learning solutions in a multidisciplinary industrial environment.
 * Advanced programming skills and experience with data cleansing, ETL and statistical and machine learning methods, including model evaluation.
 * The ability to design, validate and communicate data-driven solutions in collaboration with business, engineering and technical stakeholders.
 * Strong presentation and stakeholder management skills, including the ability to explain complex analysis and model results clearly.
 * Experience guiding, mentoring or reviewing the work of other Data Scientists.

## Description

As a Senior Data Scientist at ASML, you will work within Development & Engineering, in close collaboration with the Data Science & Engineering and Predictive Maintenance & Diagnostics groups. You will connect advanced analytics and machine learning with ASML's semiconductor development environment., * Exploratory data analysis: Collect and transform complex data from different sources, define and validate data source requirements, and scope analyses around business objectives and success criteria.

 * Advanced analysis: Design and perform tailored analyses using advanced statistical methods. Review and guide analysis tasks, explain findings to business stakeholders and lead the design of analysis tools.
 * Machine learning: Define problems with stakeholders; design and evaluate custom algorithms, models and complete Data Science solutions. Set validation methods that address reliability, robustness and higher-level requirements.
 * Model readiness: Assess training data and design models with reuse, extensibility and productisation in mind. Explain model benefits and risks to project stakeholders and subject-matter experts.
 * Software development and ML engineering: Build widely used software components, contribute to machine learning coding standards and work with ML Engineers to support the transition of algorithms into products.
 * Documentation and communication: Represent ASML Data Science in product and design reviews and at events. Document investigations, feasibility and analysis; communicate technical results to engineers, Data Scientists, architects and technology leaders.
 * Technical quality and innovation: Contribute to technical reviews, product and function design documentation, and ASML's intellectual property portfolio.
 * Coaching and leadership: Mentor and guide Data Scientists, lead small to medium-sized Data Science teams and contribute to the competence roadmap.