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About the Role

As a working student in Data Science and Advanced Analytics for energy plants, you will help develop the technical monitoring of tomorrow. The Monitoring Performance and Technology department combines engineering understanding of plants with data-driven analysis methods to identify risks early, ensure plant availability, and leverage optimization potential.

Responsibilities

Support research, evaluation, and practical testing of new monitoring and analysis methods for energy generation and district heating.
Prepare operational and condition data for analyses and model development, investigating various data and information sources.
Collaborate with the team to develop data analysis and monitoring prototypes using tools like Python, SQL, Power BI, or similar.
Participate in the development and testing of methods for anomaly and early fault detection, condition and degradation assessment, and performance and efficiency analyses.
Investigate the application of AI and data-driven models for technical analyses, forecasts, and decision support.
Compare new methodological approaches regarding their validity, data requirements, interpretability, robustness, and practical feasibility.
Develop demonstrators and prototype solutions and assist in their integration into monitoring, dashboards, and existing workflows.
Document and present procedures, results, and findings clearly, supporting technical exchange with internal and external partners.

Requirements

Enrolled student, preferably in Data Science, Computer Science, Computational Engineering, Energy and Process Engineering, Process Engineering, Electrical Engineering, Automation Engineering, Mathematics, Physics, Industrial Engineering with a technical or data analysis focus, or a comparable field.
Interest in practically testing new methods with real technical challenges.
Initial programming experience, preferably with Python.
Good mathematical-technical understanding and the ability to critically question analysis results.
Methodical approach with clear documentation of data basis, assumptions, model logic, validation, and limitations.
Ability to work independently on new topics, tools, and technical processes.
Skill in presenting complex issues clearly and collaborating with different disciplines.
Very good German language skills (written and spoken, at least C1).
Minimum of 6 months availability.
Minimum of 20 hours per week commitment.

Skills

Python
SQL
Power BI
pandas
NumPy
SciPy
scikit-learn
Time series analysis
Statistical modeling
Machine learning
Data visualization
Databases
Git
Azure DevOps
Databricks
AVEVA PI System
Thermodynamic modeling
Physical modeling
Good English skills (advantageous)
Knowledge of power plant processes, energy plants, or district heating systems (helpful, not mandatory)

Location

Berlin

Work Type

Working student
Home office possible

Experience Level

Working student

Education Level

Enrolled student

Benefits

Flexible work schedule with home office option
Provision of necessary technical equipment
Individual development and networking opportunities
Improved entry opportunities after graduation
Organic company restaurant
Free drinks (water, tea, coffee) and fruit at most locations
Various health-related offers (e.g., support from PME Familienservice)

About the Company

BEW Berliner Energie und Wärme GmbH provides electricity and heat, operating the largest district heating network in Western Europe.
With around 2,400 employees, the company covers the entire value chain of district heating, from generation to sales.
BEW is committed to phasing out coal by 2030 and achieving climate-neutral heat generation by 2045.
The company is expanding the use of renewable energies and unavoidable waste heat in its district heating system, requiring significant infrastructure changes.
BEW is seeking dedicated individuals to contribute to a local heat transition.

Equal Opportunity

Applications from individuals of all ages, gender identities, sexual orientations, or ethnic-cultural backgrounds are welcome.
People with severe disabilities are given special consideration if they have the same qualifications.
A pre-employment screening is part of the recruitment process.