Internship 2026 - Data Science and Machine Learning/AI Practitioner - Topic: Data Attribution (M/F/N)
仕事内容
You will investigate data attribution techniques to quantify how training data points contribute to model performance.
A literature review will be followed by hands-on experiments comparing libraries and methods across classical ML and DL models.
Applications will cover tabular models (XGBoost, LightGBM) and DL approaches for tabular or text data (TabPFN, TabICL, Mistral, Gemma).
You will ensure code quality, implement robust experiments, and document results for potential deployment.
Use-cases include detecting label errors and integrating attribution insights into analytics pipelines.
This internship can lead to a data science/AI role for high-potential candidates.
A literature review will be followed by hands-on experiments comparing libraries and methods across classical ML and DL models.
Applications will cover tabular models (XGBoost, LightGBM) and DL approaches for tabular or text data (TabPFN, TabICL, Mistral, Gemma).
You will ensure code quality, implement robust experiments, and document results for potential deployment.
Use-cases include detecting label errors and integrating attribution insights into analytics pipelines.
This internship can lead to a data science/AI role for high-potential candidates.
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