Under the guidance of senior engineers and data scientists, the intern will contribute to using the gold vehicle as a basis for training machine learning and deep learning models. A key part of the role involves supporting activities such as data analysis, cleaning, and structuring large datasets that are currently unorganised or only partially usable for model training. The intern will also support the connection between engineered data and product problem-solving activities, providing support for performance analysis and root cause identification.

The internship is open to students or recent graduates with a degree in computer science, data science, applied mathematics, or related fields, interested in machine learning and data engineering. The ideal candidate should have a basic understanding of data analysis concepts and awareness of the impact that data quality can have on machine learning results. Familiarity with managing large amounts of data or unstructured data, as well as an interest in anomaly detection, diagnostics, or product performance analysis, is desirable.

The role requires a curious and analytical approach, attention to detail, and a willingness to learn, as well as the ability to collaborate with multidisciplinary teams in a structured and engineering-oriented environment.

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Under the guidance of senior engineers and data scientists, the intern will contribute to using the gold vehicle as a basis for training machine learning and deep learning models. A key part of the role involves assisting in the analysis, cleaning, and structuring of large datasets that are currently unorganized or only partially useful for model training. The intern will also help connect engineered data to product issue resolution activities, supporting performance comparisons and preliminary root cause investigations.

This internship is intended for students or recent graduates with a background in IT engineering, computer science, data science, applied mathematics, or a related field, who are interested in machine learning and data engineering. The candidate should have a basic understanding of data analysis concepts and an awareness of how data quality can impact machine learning results. Familiarity with handling large or unstructured datasets, along with an interest in anomaly detection, diagnostics, or product performance analysis, is desirable. The role requires a curious and analytical mindset, attention to detail, and a willingness to learn, as well as the ability to collaborate with multidisciplinary teams in a structured, engineering-driven environment.

Bachelor's or Master's Degree Student

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