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Researcher in Predictive Modelling

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EUR 40 000 - 60 000
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Researcher in Predictive Modelling
ArcelorMittal US
Avilés
Presencial
EUR 40.000 - 60.000
Tempo integral
Há 30+ dias

Resumo da oferta

A leading steel manufacturing company in Asturias seeks a Data Scientist to join its Digitalization team. Candidates should bring expertise in machine learning and data analysis to tackle real-world industrial challenges. Familiarity with signal processing, condition monitoring, and proficiency in Python are essential. The role offers the chance to contribute to cutting-edge industrial technology initiatives while collaborating in a multidisciplinary environment. Proficiency in English is mandatory, and a willingness to learn Spanish is desired.

Qualificações

  • Master's or PhD in a related technical field.
  • Strong machine learning and statistical modeling background.
  • Proficient in Python for data analysis.

Responsabilidades

  • Apply data science and machine learning to industrial challenges.
  • Work with sensor data and develop smart diagnostics.
  • Collaborate in fast-paced R&D settings.

Conhecimentos

Machine learning
Data science
Signal processing
Python
Time-series analysis
Condition monitoring

Formação académica

Master’s or PhD in Data Science, Computer Science, Industrial Engineering, Applied Mathematics

Ferramentas

Relevant Python libraries for data analysis
Cloud-based data architectures
Descrição da oferta de emprego

ArcelorMittal is the world’s largest steel and mining. We use the most innovative technologies to create the steels tomorrow’s world will be made of. Every day over 125,000 of our talented people, located in over 60 countries, push the boundaries of digitalization, and use advanced technologies to create a stronger, faster, and smarter world. To help make this possible, they know they can depend on the support and training that a company of our scope and scale can provide.

Comprising 1,650 researchers spread across 14 Research Centers around the world, our Global R&D team aims to continuously improve the quality of steel and its manufacturing processes. This involves investigating and developing disruptive technologies, reducing the carbon footprint of steel, and optimizing costs while maintaining uncompromising quality.

The team's strong multicultural diversity, with over 30 nationalities, offers international experience and makes it possible to meet global needs.

The Digitalization team is part of the ArcelorMittal R&D Spain Lab and it provides worldwide service within the ArcelorMittal Group. We are a team of more than 70 researchers working on Digitalization and Artificial Intelligence applied to a wide range of activities (manufacturing, product development, environmental, decarbonization, supply chain, commercial, planning & scheduling, etc.). This multidisciplinary team covers a wide variety of scientific and business areas with highly qualified researchers (engineers, mathematicians, physicists, etc.), all of them with deep expertise in combining Science and Business know-how. Their enthusiasm and commitment create an incredible working atmosphere for those that want to enjoy the experience of researching and applying breakthrough ideas in the real industrial world.

Main responsibilities

We’re looking for a curious and driven individual with a strong technical background to join our team and help shape the future of industrial intelligence. You’ll bring expertise in data science, machine learning, and signal processing, and apply it to real-world challenges in manufacturing and industrial environments. Whether it’s working with sensor data, uncovering patterns in time-series signals, or developing smart diagnostics for industrial machinery, you’ll be at the forefront of innovation. You should be comfortable navigating complex datasets, building scalable solutions, and collaborating across disciplines in fast-paced R&D settings. A passion for industrial technologies, condition monitoring, and the evolving landscape of Industry 4.0 will help you thrive in this role. If you enjoy solving tough problems, working with cutting-edge tools, and contributing to multiple research initiatives, we’d love to hear from you.

Requirements
  • Master’s or PhD in Data Science, Computer Science, Industrial Engineering, Applied Mathematics, or a related field
  • Strong foundation in machine learning, deep learning, and statistical modeling, including experience with Bayesian methods and probabilistic algorithms.
  • Proficiency in Python and relevant libraries for data analysis, machine learning, and signal processing.
  • Experience with time-series analysis, anomaly detection, and condition monitoring.
  • Solid understanding of signal processing techniques and their application to industrial data.
  • Experience with sensor data acquisition systems and industrial instrumentation.
  • Familiarity with diagnostics technologies such as vibration analysis, current analysis, acoustic monitoring, and other condition monitoring methods.
  • Knowledge of various industrial assets (e.g., motors, pumps, fans), their typical failure modes, and how these manifest in different monitoring technologies.
  • Experience working with large-scale datasets from industrial systems and IoT environments.
  • Knowledge of cloud-based data architectures and scalable data processing.
  • Comfortable working in multidisciplinary teams and collaborative R&D environments.
  • Strong interest in industrial manufacturing processes and Industry 4.0 applications.
  • Well-organized and capable of contributing to multiple research initiatives in parallel.
  • Proficiency in English is mandatory and good level or willingness to learn Spanish is desired

Join us and you’ll see your work help create renewable energy, impact major industries and boost economies. At ArcelorMittal we’ll help you make your world.

* O salário de referência é obtido com base em objetivos de salário para líderes de mercado de cada segmento de setor. Serve como orientação para ajudar os utilizadores Premium na avaliação de ofertas de emprego e na negociação de salários. O salário de referência não é indicado diretamente pela empresa e pode ser significativamente superior ou inferior.

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