Employment Information
Data Scientist
Job level | Team Leader / Supervisor |
Salary | $ Competitive |
Deadline to apply | 17/05/2024 |
Industry | Mechanical / Auto / Automotive , IT - Software , IT - Hardware / Network |
Experience | 3 - 6 Years |
24/04/2024
Mechanical / Auto / Automotive , IT - Software , IT - Hardware / Network
Permanent
Competitive
3 - 6 Years
Team Leader / Supervisor
17/05/2024
• Design and implement large-scale data solutions using Cloud technologies
• Implement and manage ETL processes, data transformations, data flows and service APIs
• Work with data virtualization, integration, and analytics technologies on our data platform
• Execution of analysis in the area of production and analytics
• Development of Machine- and Deep Learning solutions in the smart factory area
• Work in interdisciplinary, cross-functional teams according to agile methodology
• Closely collaborate with divisional business organizations, digitalization, and IT functions
• In addition to the duties listed above, the position holder must carry out tasks assigned by his supervisor that are essentially related to his duties.
• Bachelor or master’s degree in computer science, statistics, engineering or related area
• Relevant work experience (>5 years) in the field of quantitative analytics or machine learning in the context of the manufacturing industry
• Hands-on experience with applying machine learning algorithms and optimization techniques, as well as a profound understanding of the mathematical concepts of these algorithms
• Good programming skills with Python
• Familiarity with modern tech stacks and ecosystems (e.g. ML Ops)
• Familiarity with Cloud platforms e.g. AWS, Azure, Google Cloud
• Knowledge of ETL techniques and frameworks, such as ADF, PowerCenter, NiFi, Sqoop
• Good communication skills and experiences in an agile work environment
The followings skills are considered as a plus and potential candidate differentiators:
• Proficiency in database and system administration (Linux, Windows)
• Experience with data virtualization architectures and platforms (e.g., Denodo)
• Experience with data warehouse design and development
• Know-how and practical experience with IoT, machine connectivity and messaging infrastructure (nats.io, e.g.)
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