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Data Scientist (f/m/d) Industrial AI

Siemens

Erlangen

Vor Ort

EUR 65.000 - 90.000

Vollzeit

Vor 3 Tagen
Sei unter den ersten Bewerbenden

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Zusammenfassung

A leading company's Smart Infrastructure electrification and automation division is seeking a data-driven professional to transform its manufacturing networks through AI. Responsibilities include leading AI projects and analyzing data to enhance efficiency. Ideal candidates hold a Master's in Data Science and possess strong machine learning expertise.

Leistungen

Attractive remuneration package
Access to Siemens share plans
30 days of paid vacation
Flexible work schedules
Mobile working options
Flexible training opportunities

Qualifikationen

  • Extensive experience in manufacturing and digitalization.
  • Effective communication abilities for complex topics.
  • Experience in handling real-world data challenges.

Aufgaben

  • Leading digitalization and AI projects with factories.
  • Analyzing manufacturing and process data to improve efficiency.
  • Promoting a data-first mentality in the manufacturing network.

Kenntnisse

Machine Learning
Data Science
Computer Vision
Generative AI
Time Series Analysis
Knowledge Graphs
IoT Data
MLOps

Ausbildung

Master's degree in Data Science

Tools

AWS

Jobbeschreibung

Are you passionate about transforming our global manufacturing network through data-guided operations? Join our team and play a key role in bringing industrial AI to the shopfloor.

What We Offer You
  • An attractive remuneration package
  • Access to Siemens share plans
  • 30 days of paid vacation and flexible work schedules for you and your family
  • 2 to 3 days of mobile working per week as a global standard
  • Flexible training opportunities for your professional and personal development

For more information on our benefits, please visit our benefits portal. Benefits are subject to regulatory, contractual, or corporate conditions.

You’ll make a difference by
  • Leading digitalization and AI projects with our factories, from developing custom solutions to evaluating third-party offerings
  • Analyzing manufacturing, machine, and process data to improve efficiency, quality, and delivery performance
  • Collaborating with factories to address business needs and conducting ideation workshops
  • Promoting a data-first mentality within our manufacturing network by establishing architecture, processes, and tools for data science and analytics
  • Implementing industrial AI on the shopfloor, including machine connectivity, data engineering, MLOps in our cloud-native platform, and model deployment on the shopfloor using our industrial edge portfolio
This Is How You'll Win Us Over
  • Holding a Master's degree in Data Science with extensive experience in manufacturing and digitalization
  • Having a good understanding of manufacturing processes, machines, IoT data (PCBA and cabinet production), and handling real-world data challenges
  • Possessing hands-on experience in machine learning and data science, especially computer vision, generative AI, time series analysis, and knowledge graphs; experience with AWS and MLOps is a plus
  • Ability to communicate complex data science topics clearly to all levels, from process engineering to management
  • Effective interpersonal skills for working with global, multi-functional teams and proficiency in German and English

We value your unique personality and perspective. We are committed to diversity and support your personal and professional growth. We welcome applications from individuals with disabilities.

About Us

At Siemens Smart Infrastructure Electrification & Automation, our mission is to develop innovative, sustainable solutions that save energy and reduce carbon footprints across buildings, infrastructure, and industries. We aim to facilitate the transition to a sustainable energy system, ensuring reliable and affordable energy for all. Join us in creating a greener future and transforming the way we live and work.

Learn more about careers at Siemens at siemens.de/careers.

For further application information, visit our FAQ section.

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