Data Science Senior/Staff Engineer

The ams OSRAM Opto Semiconductors business offers high-performance opto semiconductor components and in-depth support for state-of-the-art system solutions based on innovative semiconductor light sources. The Business Unit can look back on almost fifty years of production and development expertise.

• Conduct data‑centric evaluations and analytical studies to support R&D projects/initiatives, technology development and/or validation activities.
• Collaborate with cross‑functional R&D teams to translate engineering and/or scientific ideas/problems into analytical solutions.
• Perform data-driven asset utilization using Cloud/Big‑Data technologies to enable scalable data exploration, processing and advanced analytics.
• Generate clear and impactful visualizations, dashboards and analytical reports to communicate findings to technical and non‑technical stakeholders.
• Ensure data quality, consistency, and traceability through sound data management and governance practices.
• Contribute to the continuous improvements of data science methodologies, tools, and best practices within the R&D environment.
• Bsc/MSc/PhD in Data Science, Statistics, Computer Science, Applied Mathematics, Physics, Engineering or a related quantitative field.
• Experience working in R&D/technology-driven environments (e.g., engineering, manufacturing, electronics) with a strong focus on measurable business impact.
• End-to-end analytics project experience: data sourcing/ETL, feature engineering, modeling, deployment/automation and stakeholder reporting; familiarity with agile ways of working.
• Primarily an individual contributor with the ability to lead workstreams, mentor junior colleagues and influence cross-functional teams without formal authority.
• Comfortable collaborating in global, multicultural teams across sites and time zones; experience working with international stakeholders is a plus.
• Strong foundation in statistics and machine learning (regression/classification, time-series, clustering, optimization), experimental design and model validation, etc.
• Structured problem solving (hypothesis-driven analysis/CRISP-DM), reproducible analytics, documentation and peer review; data quality checks and governance/traceability practices, etc.
• Python (pandas, NumPy, scikit-learn) and/or SQL required; Big Data tools (Spark/Databricks) and Cloud platforms (Azure/AWS/GCP) preferred; Git, APIs, Docker/CI-CD basics; Power BI/Tableau for dashboards, etc.
• English – fluent (spoken and written). Additional local language(s) beneficial.
• Excellent communication and data storytelling, strong collaboration and stakeholder management, curiosity and learning agility, able to translate engineering problems into analytical solutions, etc.
• High standards for confidentiality and handling sensitive data; proactive continuous-improvement mindset, willingness to travel if needed, etc.