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Staff Data Scientist/Steward or Software Developer

TU München · München, Freising | München | Deutschland | Freising · Onsite

Gehalt auf Anfrage

Gefunden am 03.06.2026

Match

84%

Fit in Skills, Kultur und Entwicklungspfad.

Beschreibung

Deine Rolle im Team Service engineering and infrastructure maintenance for ProteomicsDB. Full-stack development to enhance ProteomicsDB. Data import, maintenance, and integration of e.g. proteomics, lipidomics, or metabolomics data. Unsere Erwartungen an dich Ausbildung Candidates must hold a master's degree in Data Engineering, Data Science, Bioinformatics, Informatics, or a related discipline. Qualifikationen Essential skills include a sound understanding of Linux and related systems, software development standards, and hardware management. Additional beneficial skills include a theoretical knowledge of and practical skills in statistical analysis, data integration, backend or frontend programming and database design. Interest in understanding technologies e.g. proteomics and metabolomics using mass spectrometers is expected. We are looking for a self-motivated and broadly interested individual with high potential and a strong sense of responsibility. Flexibility and the ability to work in a fast-paced environment on multiple scientific and infrastructure projects are essential. Good inter-cultural and inter-personal communication skills as well as the ability to present in English are also important. Benefits Gesundheit, Fitness & Fun 🎳 Team Events Work-Life-Integration 🏠 Home Office 🚌 Gute Anbindung 🍼 Kinderbetreuung ⏰ Flexible Arbeitszeiten Mehr Netto 👴🏻 Betr. Altersvorsorge Themen mit denen du dich im Job beschäftigst Big Data Metadaten Level: Erfahren Job Feld: Software, Data Anstellung: Vollzeit Vertragsart: Befristetes Dienstverhältnis Arbeitsmodell: Onsite Unternehmenstyp: Etablierte Firma Branche: Bildungswesen Ort: München, Freising | München | Deutschland | Freising

Tech Stack

Keine Tech-Tags verfügbar.

Warum passt du zu dieser Stelle?

Fit technisch: Stark auf Backend und API-Architektur.

Gaps: Fehlende Tool-Erfahrung in 1-2 Schlüsselbereichen.

Success-Wahrscheinlichkeit: Hoch bei schneller Einarbeitung.