Data Scientist

**Introduction to the job**:
**Role and responsibilities**:

- Identify and understand data sources (in complex environment)
- Collect large heterogeneous data from various sources
- Preprocess: Structure and integrate data sources
- Explore: Visualize, summarize, and conclude, e.g.
- Use and develop Scientific Visualizations
- Summary and interactive visualizations
- Design one or more fit-for-purpose and cross-domain data science solutions or select from available off-the-shelf solutions and test specifications
- Lead the design of analyses and clearly communicate benefits and risk of the developed analysis to a larger stakeholder group
- Data-driven Modeling, with focus on:

- Learning empirical models from data using statistical modeling and machine learning with thorough analysis of parameter search, feature engineering, etc.
- Dealing with uncertainties and data quality issues
- Using Prior knowledge from Domain experts and Physical models
- Discovering (causal) relationships and detecting structural patterns from data
- Define and execute validation methods that comply to business requirements, system level specifications and/or domain expectations
- Interface with domain experts to implement validation methods
- Provide an explanation and interpretation for the model and its behavior to domain/business expert/owners
- Interface with the data engineers to deliver (step 2,3,6)
- Integration of pipeline with software stack (ASML)
- Automation of data flow/pipeline
- Scalable Pipelines
- Communication to stake holders and customers/users/peers
- Knowledge Elicitation
- Story telling
- Mentor, supervise and guide junior data scientists

**Education and experience**:
**Degree**: Master/PhD, preferably on the cross-section of technology/engineering and business/economics (e.g., econometrics, industrial engineering, operations research, supply chain management, applied mathematics, physics, data science)

**Years of experience**:If MSc,** **6+ years, if PhD, 3+ years of experience using data science (experienced professional) in working with data, techniques, analysis tools and statistics.

**Skills**:
Working at the forefront of tech, you will always have new challenges and unfamiliar problems to solve and working together is the only way to do that. You will not work in a silo. Instead, you will be part of a creative, dynamic work environment where you will collaborate with supportive colleagues. There is always space for creative and unique points of view. You will have the flexibility and trust to choose how best to tackle tasks and solve problems.

To thrive in this job, you will need the following skills:
**Hard skills**:

- Affinity with data and business intelligence platforms, data analysis and analytics tools and techniques
- Knowledge of statistics and experience using statistical packages for analyzing large datasets (Excel, SQL, R, Python, etc.)
- Knowledge of data modelling, cleansing and transformation using data science packages (e.g., pandas, polars, etc.)
- Validate suitability of training datasets for ML task
- Design and perform specialized tailor-made analysis required for the use case or perform state-of-the-art advanced statistical analysis
- Design and develop ML models with considering reusability, extensibility, and productization effort
- Design of validation method for ML solutions considering higher level requirements, reliability, and robustness
- Design and build SW components (mostly in Python) with widespread usage according to best practice
- Close collaboration with ML/Data Engineers to ensure seamless transfer of code between teams
- Good coding skills and good experimentation/investigation skills
- Good algorithm development and implementation skills

**Soft skills**:

- Strong capability for problem solving, thinks creative, out of the box
- Perseverance to ‘find the needle in the haystack’
- Able to work effective and constructive in multidisciplinary teams
- Professional communication: can explain and present technical matters within team
- Internal drive for efficiency and able to work disciplined and accurate
- Pragmatic, pro-active, ‘self-propelling’ way of working and able to operate in a dynamic environment
- Take responsibility: say what you do and do what you say
- Transparent and self-reflective: learn from your own mistakes
- Observe the wishes and needs of the customer, interpret them and then act accordingly.

**Desirable**:

- Hands-on experience with PowerBI, Python (including ML libraries, spark, pandas and/or polars), Azure ML, Spotfire, R
- Experience with Dagster & LakeFS a pre
- Experience with MLOps and Agile (SCRUM)
- Ability to deploy models (e.g., cloud deployment)

**Other information**:
The sector Customer Support (CS) is responsible for the maintenance, repair, and continuous improvement of ASML systems at customer locations, as well as transferring all relevant knowledge and supporting the customer in the use of these systems in his p