Simulation Data Analytics Specialist

Quick Facts

• Role: Simulation Data Analytics Specialist (mathematics/physics focus; not primarily programming)
• Domain: External aerodynamics and simulation analysis (methods transferable to other simulation types)
• Typical workload: 10–100 tasks per dataset (small sample size)

Description

You will design and validate analysis methods that support fact-based decision-making from simulation data. The main challenge is building approaches that remain defensible with small sample sizes, then translating Python prototypes into production-ready implementations. You will also work with CFD engineers to identify where the analysis is difficult and prioritize improvements.

Responsibilities

• Design analysis methods: correlation and sensitivity analysis; POD/PCA dimension reduction; conditional statistics and clustering within planes; anomaly detection; surrogate models.
• Decide which methods are statistically justifiable for the given sample size and avoid methods that only look convincing.
• Write specifications for developers: inputs, outputs, assumptions, edge-case behavior, and acceptance criteria.
• Build Python prototypes that form the basis of production implementations.
• Validate that production code computes the correct quantities, including tests with synthetic data with known outcomes.
• Define result presentation and interpretation, including warnings about when results should not be trusted.
• Collaborate with CFD engineers to find where analysis breaks down and set priorities accordingly.

Requirements

• Experience designing methods such as correlation/sensitivity analysis, POD/PCA dimension reduction, conditional statistics and clustering, anomaly detection, and surrogate modeling.
• Ability to evaluate which methods are credible for small datasets (10–100 tasks) vs. which produce misleading “nice-looking” outputs.
• Ability to write clear technical specifications for developers (inputs/outputs, assumptions, edge cases, acceptance criteria).
• Ability to prototype in Python and validate implementations with tests on synthetic data with known results.
• Ability to define how outputs are displayed and interpreted, including when to distrust them.
• Experience working with CFD engineers to identify friction points in the analysis.

Benefits

• Long-term cooperation in a challenging technical field and opportunity to work on varied projects.
• Flexible schedule with option to work from Home Office.
• Health leave/sick days and competitive compensation.
• Indefinite-term contract, 5 weeks of vacation.
• Education support and contributions to professional development.
• Meal allowance, pension insurance contribution, and additional benefits.