Engineering Data Scientist: AI-Driven Simulations

Multi-Wing International; Denmark; On-site; DKK 600,000 - 900,000; Full time

Benefits offered by this job

Competitive compensation

Career development

International work environment

Job summary

Multi-Wing International seeks an Engineering Data Scientist to advance data-driven simulations for industrial ventilation and cooling. You will work with engineering teams to translate requirements into data models and workflows, handling large datasets and integrating AI/ML models with simulation environments.

The role emphasizes collaboration, tool training, and leveraging AI/ML to push engineering insights and efficiencies across the organization.

Qualifications

• 3–5 years of hands-on data processing experience.
• Knowledge of state-of-the-art AI/ML/DL frameworks.
• Ability to translate complex technical results into engineering insights.
• Ability to work cross-functionally with engineering, IT, and product teams.

Responsibilities

• Develop, validate, and deploy data-driven simulation tools for product design and validation.
• Collaborate with engineering teams to translate requirements into data models and workflows.
• Collect, preprocess, and manage large engineering datasets including 3D geometries and CFD/FEM results.
• Integrate AI/ML models with existing simulation and design environments.
• Train engineers on the use of AI-driven tools to scale knowledge.
• Stay updated with AI/ML advancements and propose innovative methods.

Skills

Data processing

AI/ML frameworks

Cross-functional collaboration

English communication

Problem solving

Education

Degree in Data Science/Computer Science/Engineering

Job description

Multi-Wing International seeks an Engineering Data Scientist to advance data-driven simulations for industrial ventilation and cooling. You will work with engineering teams to translate requirements into data models and workflows, handling large datasets and integrating AI/ML models with simulation environments.

The role emphasizes collaboration, tool training, and leveraging AI/ML to push engineering insights and efficiencies across the organization.