Internship Machine Learning & Predictive Analytics for Heat Pump

Job Description:

Job Description

* During your internship you will analyze large-scale historical data to uncover patterns, correlations, and anomalies that support product reliability improvement.
* You will design and evaluate meaningful new features from raw data; perform correlation and relevance analysis to identify key predictive indicators.
* Furthermore, you will develop and evaluate machine learning models on the Databricks platform to enable early detection of potential product issues and proactive reliability improvement.
* Finally, you will present findings and model results clearly through visualizations and dashboards to support engineering decision-making

Candidate Requirements:

Qualifications

* Education: Master studies in the field of Data Science, Machine Learning, Statistics, Computer Science, Mathematics, or comparable
* Experience and Knowledge: in-depth knowledge of machine learning (supervised learning, model evaluation, cross-validation, hyperparameter tuning, and understanding of when and why to apply different algorithms); solid understanding of feature engineering principles (feature extraction, selection, correlation analysis, and strategies for handling imbalanced or noisy data); proficient in Python with working knowledge of Pandas, NumPy, Scikit-learn, Matplotlib/Seaborn; strong foundation in statistics and data preprocessing techniques; experience with Databricks, PySpark, MLflow, or time-series analysis concepts is a plus; familiarity with MLflow or time-series analysis concepts is an advantage
* Personality and Working Practice: you excel at being a self-driven and curious individual, capable of independently exploring data, formulating hypotheses, and iterating on solutions; you approach analysis with a structured and rigorous mindset and are skilled at clearly explaining complex findings to non-technical colleagues
* Work Routine: hybrid model (on-site presence required at least 2 days per week, mobile working available for the rest)
* Enthusiasm: passionate about applying machine learning to real-world product data
* Languages: very good in English