Production Data Scientist: AI & ML for Smart Manufacturing

Outokumpu; Helsinki; Sur place; EUR 60 000 - 90 000; Plein temps

Résumé du poste

Outokumpu seeks a Data Scientist to join the Enterprise Intelligence Platforms IT team. You will develop AI/ML models to optimize production, improve quality, and streamline procurement across stainless steel and ferrochrome operations.

Collaborating with site engineers, R&D, and other teams, you’ll deploy models, communicate results clearly, and contribute to project planning. A background in manufacturing analytics and data science is essential.

Qualifications

• Master’s or Bachelor’s degree in a relevant field.
• At least two years practical experience in data‑science domain.
• Expertise in AI/LLMs (incl. deep learning), machine learning, optimization, statistics or computer vision.
• Knowledge of Python, R or similar programming languages is mandatory.
• Experience using AI coding tools in modelling and software development is a plus.
• Familiarity with basic developer tools and software development principles.

Responsabilités

• Develop AI and machine learning models for production optimization and product quality.
• Create time series and computer vision algorithms for production processes.
• Productize ML/AI models by integrating them into applications or interfaces.
• Communicate results to stakeholders and participate in planning (epics, features, user stories).
• Maintain high code quality and participate in planning and governance.

Connaissances

Python

R

AI/ML tools

Data modelling

Formation

Master’s or Bachelor’s degree in a relevant field

Doctoral degree (advantage)

Outils

Databricks

Azure

Description du poste

Outokumpu seeks a Data Scientist to join the Enterprise Intelligence Platforms IT team. You will develop AI/ML models to optimize production, improve quality, and streamline procurement across stainless steel and ferrochrome operations.

Collaborating with site engineers, R&D, and other teams, you’ll deploy models, communicate results clearly, and contribute to project planning. A background in manufacturing analytics and data science is essential.