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Student Assistants: Energy Data Analysis

vor 4 Monaten


Berlin, Deutschland Agora Energiewende Vollzeit

**Agora Energiewende**:

- Agora Think Tanks are pursuing the goal of achieving climate neutrality worldwide by the middle of the century. With currently around 150 employees at four locations worldwide, we combine research, dialogues, and technical assistance. We inform policymakers, economists, researchers, and civil society while encouraging a productive exchange of ideas. Our policy proposals are practical and free of prior ideological commitments. We are a non-profit organisation funded by foundations and public institutions and hence are beholden neither to business nor to politics. Our exclusive mission is to serve the climate.
Agora’s Data Visualisation, Analytics, and Modelling team leads the development of database services, analytical and modelling tools, as well as visualisation products for Agora Energiewende, Agora Industry, and Agora Agriculture. It is a growing cross-organisational team working at the intersection of our sectorial, policy and communication work in all geographies (Germany, EU, international). To strengthen our team, we are looking for
- Student Assistants: Energy Data Analysis & Modelling (f/m/d)

15 to 20 hours per week - Berlin-based

to support us in the backend development tasks, including database management and Python-based modelling activities.

**Working field**:

- Supporting the development and maintenance of databases for power/hydrogen systems and energy data of key emerging economies and OECD countries in close collaboration with our teams;
Collecting, processing, and cleaning data from various sources and storing it in our databases (i.e., manual entry, automated feed, and through APIs);
Contributing to Agora’s model development for power/ hydrogen system analysis and data processing;

Supporting colleagues in data access and quantitative power/hydrogen system analysis;
Working closely with our experienced team and partner organisations in the respective countries to provide support, training and collaborate on research projects.

**Requirements**:

- A motivated student from a higher education institution in informatics, computer science, statistics, data or natural science, engineering, or a similar degree (enrolled at least for the next 12 months and ideally for the next 24 months);
Excellent command of Python, and preferably, practical experience with pandas, NumPy, SQLAlchemy, Frictionless, and DataPrep packages as well as the PyPSA modelling framework;
Expertise in SQL language, particularly in PostgreSQL and MySQL database management systems;
Experience with version control systems such as Git;
Strong proficiency in Microsoft Office products, particularly in the development of advanced VBA macros;
Deep understanding of energy and industry decarbonisation topics and datasets and knowledge in collecting, structuring and interpreting them;
Strong problem-solving skills and attention to detail, excellent communication skills, and the ability to work effectively in a team environment;
Fluency in written and spoken English; knowledge of German is advantageous.

**What we offer**:

- An opportunity to work in a mission-driven, highly motivated and professional team on an essential task for the future;
A diverse and international working environment in a cooperative working atmosphere with flat communication and decision-making hierarchies;
An attractive and comfortable workplace in central Berlin with fresh fruit and coffee, weekly breakfast, team events and other get-togethers with colleagues;
Opportunities for professional growth and advancement;
The option of hybrid working with good digital equipment.
A comprehensive onboarding programme, free access to a mental health coaching platform;
A student employment contract initially limited to one year with attractive remuneration.

**How to apply**:

Please use „Application_Student_Assistant Data Analysis and Modeling_surname_name”. Please include a letter of motivation and your CV and indicate your salary per hour expectations as well as your earliest possible starting date. Please mention the specific team if you have a preference.