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.
Requirement
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.
