Internship Machine Learning & Predictive Analytics for Heat Pump at Bosch Group

Wernau (Neckar), Baden-Württemberg, Germany -

Full Time

Start Date

Immediate

Expiry Date

22 Jun, 26

Salary

0.0

Posted On

24 Mar, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Predictive Analytics, Data Analysis, Feature Engineering, Model Evaluation, Cross-validation, Hyperparameter Tuning, Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Statistics, Data Preprocessing, Databricks

Industry

Software Development

Description

Company Description At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference. The Bosch Home Comfort Group is looking forward to your application! 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. 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 Additional Information Start: according to prior agreement Duration: 6 months Requirement for this internship is the enrollment at university. Please attach your CV, transcript of records, enrollment certificate, examination regulations and if indicated a valid work and residence permit. Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity. Need further information about the job? Linnan Du (Functional Department) +49 7153 3068232 Work #LikeABosch starts here: Apply now! #LI-DNI Legal Entity: Bosch Thermotechnik GmbH

Responsibilities

The intern will analyze historical data to find patterns supporting product reliability improvement and develop/evaluate machine learning models on the Databricks platform for early issue detection. Findings and model results must be clearly presented through visualizations and dashboards to aid engineering decision-making.