At Roche Diagnostics in Rotkreuz, one of the key global sites for diagnostic innovation, a multidisciplinary team is working on advanced near-patient-care solutions for the analysis of blood gases, electrolytes and metabolites in human blood. Within the area of BGE Sensors, Reagents & Processes, the focus of this position is on supporting the development of next-generation sensor cartridge designs as well as the related manufacturing and process landscape.
This role combines data engineering, analytics, process understanding and cross-functional collaboration. It is particularly suited for someone who enjoys working at the interface of R&D and production, using data to improve technical processes and enable smarter decision-making.
Roche promotes an inclusive working environment and is committed to equal opportunity for all employees.
Key Responsibilities In this position, you will contribute to both development and process optimization activities by supporting data-driven work across engineering teams. Your main tasks will include:
Collaborating with interdisciplinary engineers on the design and optimization of thick film sensor products and manufacturing processes
Developing, improving and maintaining data pipelines and infrastructure for production and process-control data from multiple input sources
Ensuring reliable and timely access to structured data originating from systems such as SQL databases, CSV files and machine-generated logs
Serving as a technical link between R&D and Production, while also aligning data practices across different R&D functions
Creating and further developing dashboard solutions, interactive reports and data visualizations for routine analysis and operational transparency
Supporting the development of image analysis methods to extract measurable and meaningful features from image-based data
Expanding the data environment by integrating additional technical and performance-related information, including sensor characterization results
Contributing to the continuous improvement of manufacturing processes through data-based evaluation and analysis
Candidate Profile The ideal candidate brings hands-on experience from a technical development, industrial R&D or manufacturing-related environment, ideally within the medical device sector or another highly regulated industry.
You are comfortable working with technical data from different systems, understand how to structure and transform data for practical use, and can present findings in a way that supports engineering and development teams.
A strong fit for this role typically includes:
A completed education in process engineering, data science, bioinformatics, or a comparable technical field
Proven experience in industrial R&D or manufacturing, preferably in a regulated setting such as medical devices
Practical know-how in building and maintaining ETL/data pipelines across heterogeneous data sources
Strong capability in designing interactive dashboards, analytical views and visualization tools
Solid understanding of applied statistical methods, for example correlation analysis or clustering techniques
Additional value through experience in digital image processing
Good programming skills in Python, especially in an object-oriented environment
Working knowledge of SQL
Clear communication skills and confidence working in multicultural, cross-functional teams
A structured and well-documented way of working
A proactive, team-oriented mindset with the ability to move projects forward in a dynamic setting
Technical curiosity and the ability to understand and improve process-related systems
Good command of both German and English
Latest possible start: 01.08.2026
Home office: Up to 1 day per week
Team size: 14
Working hours: Standard business hours
Core Strengths Sought This opportunity is especially attractive for candidates who combine:
technical process understanding
data engineering and analytics capability
visualization and dashboarding skills
experience in regulated industrial environments
the ability to bridge engineering, production and data topics
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