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# Data Scientist

- **Company:** Nexperia Germany GmbH
- **Location:** Hamburg, Germany
- **Contract:** Permanent contract
- **Skills:** Information Systems, Data Integration, Extract Transform Load (ETL), Data Retrieval, Data Structures, Relational Databases, Python (Programming Language), DataOps, SPARQL, SQL Databases, Data Processing, Pandas
- **Published:** September 28, 2026
- **Apply:** https://www.arbeitsagentur.de/jobsuche/jobdetail/10001-1002918971-S

## About the Role

Enrolled Master's student in Data Science, Engineering, Information Systems, or similar

 - General understanding and passion for data
 - Proven experience with data-oriented programming languages (e.g. Python, or similar) and basic knowledge of querying (e.g., SQL, SPARQL, or similar)
 - Experience with relational databases and data processing (e.g. SQL, pandas-based ETL, or similar)
 - Strong analytical, structured thinking, and problem-solving skills; able to explore solutions independently and show natural curiosity
 - Good communication skills in English (German language skills beneficial) and ability to work independently
 - Basic knowledge of graph-based data models (e.g. RDF/SPARQL, or similar) is a plus
 - Basic knowledge of semiconductor products is a plus
 - Interest in sustainability, life cycle assessments, product carbon footprints, or semiconductor manufacturing

## Description

You will work on structuring and connecting complex manufacturing data to support product-level sustainability analysis. For this you will work with real industrial data and collaborate with domain experts.

The focus is on:

 1. Designing a logical data structure
 2. Understanding how different data points relate to each other
 3. Enabling data retrieval and analysis through queries

In this thesis, you will develop a structured data approach to organize and manage product-level data to support accurate Product Carbon Footprint (PCF) assessments in semiconductor manufacturing, which requires working with complex and fragmented data.

What you will do

 - Analyze existing manufacturing and product-level data to identify structures and relationships.
 - Design a structured data model to organize product-relevant data.
 - Explore and link data to support product-level carbon footprint analysis.
 - Develop queries and small-scale prototypes to test the data model.
 - Apply the model to a practical case study to demonstrate functionality and data integration (proof-of-concept).
 - Document the design, methodology, and findings in a clear and structured thesis report.
 - Present results and insights to stakeholders in a concise and professional manner.
 - Navigate uncertainty with analytical thinking and open-mindedness.


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