ML Ops Data Scientist — Remote in Portugal

Siemens Energy

Amadora

Sur place

EUR 55 000 - 75 000

Plein temps

14 jours+
Générateur de candidature

Une candidature complète en une minute — un CV et une lettre de motivation personnalisés, prêts à envoyer.

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Avantages offerts par ce poste

Remote work within Portugal
Meal allowance
Health insurance coverage
Pontes days and Birthday off

Résumé du poste

Siemens Energy is seeking a Data Scientist (Machine Learning Ops) to join the Digital Services unit at Gas Services and work on challenging projects across the energy sector. You will combine strategic insights with analytics to deliver value in day-to-day operations.

You will apply AWS SageMaker, Python, R, and Docker to build scalable analytics and AI concepts, collaborating in a global, multicultural environment and communicating results to drive data-driven decisions.

Qualifications

  • Working knowledge in Data Science, ML, and AI with AWS SageMaker expertise.
  • Programming skills in Python and/or R; Docker experience is beneficial.
  • Experience deploying analytics models using cloud services.

Responsabilités

  • Develop advanced analytics solutions across the value-chain.
  • Create data mining architectures, models, and methodologies.
  • Design AI concepts using research and trends to address challenges.
  • Apply tools across business areas and communicate findings regularly.
  • Stay updated on data science principles to support decisions.

Connaissances

Data Science
Machine Learning
AI
Statistical Modeling
Data Analytics

Outils

AWS SageMaker
Python
R
Docker
Cloud Services

Description du poste

Siemens Energy is seeking a Data Scientist (Machine Learning Ops) to join the Digital Services unit at Gas Services and work on challenging projects across the energy sector. You will combine strategic insights with analytics to deliver value in day-to-day operations.

You will apply AWS SageMaker, Python, R, and Docker to build scalable analytics and AI concepts, collaborating in a global, multicultural environment and communicating results to drive data-driven decisions.

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