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Job ID 490024 Posted since 26-Dec-2025 Organization Digital Industries Field of work Internal Services Company Siemen...

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Selected role

Part Time Working Student (Data Scientist)

Siemens İstanbul, T34, TR

$490k Senior Full-time

Job ID

490024

Posted since

26-Dec-2025

Organization

Digital Industries

Field of work

Internal Services

Company

Siemens Sanayi ve Ticaret Anonim Sirketi

Experience level

Student (Not Yet Graduated)

Job type

Part-time

Work mode

Hybrid (Remote/Office)

Employment type

Fixed Term

Location(s)

Istanbul - Istanbul - Tuerkiye

We are looking for a Part-time Working Student (Data Scientist) to join our Retrofit and Digitalization Project Team. The position will be based in Kartal and requires flexibility to travel to various locations across Turkey when needed. The successful candidate will support the execution and development of digitalization projects and will contribute to the enhancement of software solutions related to IoT, AI, and machine learning technologies.

What are my responsibilities?

Understand the customer needs and take necessary actions to create appropriate solutions
Carry out small or medium sized digitalization projects and do the commissioning of projects on site
Supply creative feedback to increase the quality-of-service operations and processes
Providing know-how and sharing with other colleagues
Supporting functions of the different automation and AI projects
Planning, reporting and improvement activities
Planning, organization and execution of start-up of operations
Supporting operational project teams
Software design, implementation, debugging, unit tests, module tests, test automation, and involving in release tasks for industrial automation projects

What do I need to qualify for this job?

3rd-year undergraduate or master’s student in Computer Engineering, Mathematics, Electrical & Electronics Engineering, Mechatronics, Automation & Control Engineering, or related field
Good command of English, both written and spoken
Availability to work at least 3 days per week
Strong interest in Industrial AI, IoT, and digitalization technologies, with a willingness to learn and grow in this area
Strong analytical thinking skills with a problem-solving mindset
Ability to communicate analytical and technical outputs in a clear way and translate them into business needs
Disciplined approach to documentation and reporting
Basic experience in data analysis and modeling with Python (NumPy, Pandas, Scikit-learn)
Fundamental knowledge of statistics and probability (e.g., distributions, correlation, regression, hypothesis testing)
Basic experience in data querying, preparation, and data cleaning
Hands-on exposure to at least one machine learning use case (regression, classification, or anomaly detection), including feature engineering and model evaluation
Interest in Anomaly Detection, Forecasting, or Predictive Maintenance use cases Openness to learning and applying end-to-end ML workflows (train validate iterate* improve)

Additional Qualifications:

Beginner-level exposure to TensorFlow or PyTorch
Interest in industrial, operational, sensor, or production data analytics

At Siemens we are always challenging ourselves to build a better future. We need the most innovative and diverse Digital Minds to develop tomorrow ‘s reality.

As Siemens we believe physical barriers are not related to potential. Only the potential matters to us. Therefore, we look forward to receiving applications from candidates with physical barriers and chronic illnesses. We support healthy relationships between candidates with barriers and their colleagues because we believe we can create differences together.

Siemens is dedicated to quality, equality, and valuating diversity and we welcome applications that reflect the diversity of the communities within which we work.

We are looking forward to receiving your online application. Please ensure you complete all areas of the application form to the best of your ability as we will use the data to review your suitability for the role.

Please find more information from our web site:

https://new.siemens.com/tr/tr.html

*#LI-HYBRID

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