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Mid-level ML role in metro with broad skills and known global brand increases competition.
Core ML skills are transferable but required automotive/manufacturing experience raises domain sensitivity.
Explicit 5+ years, required automotive experience, and mandatory ML/cloud/tool skills tighten shortlisting.
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Develop and deploy AI and machine learning solutions supporting virtual tire development processes in the Data Driven Engineering group.
Perform end-to-end data tasks including exploration, transformation, feature engineering, modeling, and visualization on large-scale datasets within global initiatives.
Collaborate cross-functionally with data scientists, engineers, cloud architects and business stakeholders to scale prototypes to production and drive adoption of data-driven engineering best practices.
M.Sc. in Data Science, Mathematics, Statistics or similar technical field; Ph.D. preferred or equivalent experience.
Minimum 5+ years in Machine Learning and advanced data analysis on large datasets.
At least 1 year experience in the automotive or industrial sector focusing on structures, vibrations, or acoustics.
Proficiency in Python, SQL, software development tools (GitHub, Docker, Kubernetes, Jenkins) and cloud platforms (AWS or Azure).
Experienced in applied mathematics, advanced statistics including Bayesian inference and uncertainty quantification, and modern machine learning techniques such as supervised/unsupervised learning and deep neural networks.
Proven ability to independently handle complex data engineering and machine learning tasks in an industrial or automotive context.
Effective collaborator with multi-disciplinary teams and capable of communicating technical concepts clearly to diverse stakeholders.