Deadline:
As soon as possible
Location(s)
Luxembourg
Esch-Sur-Alzette, 4008
Overview
Details
Responsibilities:
- Assist in data collection, preprocessing, and analysis.
- Support senior data engineers and scientists in developing and implementing data models.
- Participate in the design and implementation of data pipelines.
- Conduct exploratory data analysis to uncover insights and trends.
- Collaborate with the AI/ML team to support machine learning projects.
- Partially participate in the data engineering phase of data acquisition and refining.
ABOUT US
ArcelorMittal is the world's leading steel and mining company, with a presence in 60 countries and primary steelmaking facilities in 16 countries. In 2022, ArcelorMittal had revenues of $79.8 billion and crude steel production of 59.0 million metric tonnes, while iron ore production reached 45.3 million metric tonnes. Our purpose is to produce ever smarter steels that have a positive benefit for people and planet.
Steels made using innovative processes which use less energy, emit significantly less carbon and reduce costs. Steels that are cleaner, stronger and reusable. Steels for electric vehicles and renewable energy infrastructure that will support societies as they transform through this century. With steel at our core, our inventive people and an entrepreneurial culture at heart, we will support the world in making that change. This is what we believe it takes to be the steel company of the future. ArcelorMittal is listed on the stock exchanges of New York (MT), Amsterdam (MT), Paris (MT), Luxembourg (MT) and on the Spanish stock exchanges of Barcelona, Bilbao, Madrid and Valencia (MTS).
Eligibility
Candidates should be from:
Description of Ideal Candidate
Requirements:
- Bachelor’s or master’s degree in Data Science, Computer Science, Statistics, or a related field.
- Basic understanding of data analysis techniques and tools (e.g., SQL, Python, R).
- Familiarity with machine learning concepts (classification, regression, etc.).
- Knowledge of machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.
- Acquaintance with generative AI concepts and applications.
- Familiarity with knowledge graph theory and graph databases.
- Understanding of NLP, computer vision, and time series forecasting.
- Experience with cloud platforms like Microsoft Azure.
- Basic knowledge of data engineering tools and practices.
- Familiarity with Microsoft Fabric (nice to have).
- Knowledge of RDBMS such as Oracle, SQL Server, and others.
- Understanding of knowledge graph theories.
- Strong analytical and problem-solving skills.
- Eagerness to learn and develop new skills in data engineering and analytics.
- Willingness to stay updated with new technologies and discover emerging market trends.
- Good communication skills and the ability to work in a team environment.
Dates
Deadline: As soon as possible
Cost/funding for participants
Un-save Opportunity
Internships, scholarships, student conferences and competitions.
