Your tasks
- 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.
Who we are looking for
- MSc or 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.




















