Working Student - Data Science (20h/week)
Berlin, Germany
Everest Carbon
Location: Berlin, Germany
Contract Type: Part-time (up to 20h/week), Working Student
Help build the data infrastructure powering tomorrow’s climate solutions.
At Everest Carbon, we’re developing groundbreaking sensors that measure how much CO₂ is removed from the atmosphere through Enhanced Weathering — a scalable, natural solution to the climate crisis. Our sensors are already being used in the field, and demand is growing fast. We’re looking for a hands-on Data Science Working Student to help us analyze large volumes of sensor data and develop machine learning models to improve sensor calibration and field performance.
You'll join our R&D team at an exciting moment: scaling a breakthrough technology from 1 to 100. This is not just a typical data cleaning job — it's a technical, dynamic role where you'll explore real-world sensor data, build predictive models, validate algorithms, and help design the data pipelines that will scale our technology globally.
Tasks
- Analyze large, real-world sensor datasets from field deployments
- Develop and validate machine learning models to improve sensor calibration and data interpretation
- Build and maintain data pipelines and workflows for sensor data processing
- Support the design of algorithms for real-time sensor monitoring and diagnostics
- Perform exploratory data analysis (EDA) to identify patterns, anomalies, and improvement opportunities
- Collaborate closely with R&D engineers to translate raw data into actionable insights
- Document your work clearly for internal use and future scaling
Requirements
- You are enrolled in a Bachelor’s or Master’s program in Physics, Mathematics, Data Science, Computer Science, Engineering or a related field
- Strong experience with Python (including libraries like pandas, scikit-learn, NumPy, matplotlib)
- Solid understanding of data analysis, statistics, and machine learning fundamentals
- First experience with real-world, noisy datasets (e.g., from sensors, lab experiments, or research projects)
- Familiarity with basic software engineering practices (e.g., Git, version control, writing clean code)
- Hands-on mentality: you enjoy solving technical challenges, even when data is messy
- (Bonus) Experience with time-series data, signal processing, or calibration models
- (Bonus) Familiarity with cloud tools (e.g., AWS, GCP) or database management (e.g., SQL, InfluxDB)
Benefits
- Mission-driven work: your work will directly contribute to building climate solutions based on real, measurable impact
- Hands-on learning: work directly with our Head of R&D and CEO, gaining first-hand experience in next-generation data science methods, real-world sensor analytics, and leveraging large language models (LLMs) in practice to 10x your data science work
- Growth opportunity: as we scale, you’ll have the chance to take on more responsibility or specialize in areas like machine learning, data engineering, or scientific computing
- Startup vibe, real impact: small, collaborative team, flat hierarchy, and the opportunity to see your work go into production quickly
- Benefits: paid vacation, healthcare, retirement plan, free snacks & good coffee, and a beautiful Berlin office with lab and workshop space
We Would Love to Hear From You
We understand that you might not fit all of the requirements above perfectly. We envision our team to be as diverse as the solutions that will solve the climate crisis. Forming a compassionate workplace is a core part of our culture – including all walks of life, experiences, and perspectives. Candidates from structurally underrepresented cultures and backgrounds are especially encouraged to apply.
Apply Now
If you're ready to leverage your extensive experience to make a substantial impact on climate change, apply today!
Tags: AWS Computer Science Data analysis Data pipelines EDA Engineering GCP Git InfluxDB LLMs Machine Learning Mathematics Matplotlib ML models NumPy Pandas Physics Pipelines Python R R&D Research Scikit-learn SQL Statistics
Perks/benefits: Career development Flat hierarchy Salary bonus Startup environment
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