Application Guide

How to Apply for Working Student Data Science Forecasting (m/f/d)

at 1KOMMA5°

🏢 About 1KOMMA5°

1KOMMA5° is a fast-growing startup dedicated to making buildings CO2-neutral through integrated clean energy solutions. What sets them apart is their holistic approach—combining solar, heat pumps, EV charging, and energy management into one seamless system, with a strong emphasis on data-driven optimization.

About This Role

As a Working Student in Data Science Forecasting, you'll directly contribute to the core mission by building and maintaining time-series models that predict PV generation, energy consumption, and EV charging patterns. Your work will power real-time decisions in their energy platform, directly impacting grid stability and renewable energy utilization.

💡 A Day in the Life

You'd start by checking the performance of forecasting models in production, reviewing any alerts or anomalies. Then you might join a stand-up with the data team to discuss pipeline improvements. The rest of the day could involve coding a new feature for a model, running experiments in a Jupyter notebook, or collaborating with engineers to deploy a updated pipeline.

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Interview Prep

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🎯 Who 1KOMMA5° Is Looking For

  • A student with strong Python skills and hands-on experience with pandas, numpy, and scikit-learn, who can write clean, production-ready code.
  • Someone with a solid grasp of time-series forecasting methods (ARIMA, Prophet, LSTM) and experience training/evaluating models on real data.
  • A team player who is curious about renewable energy and eager to learn about energy systems, even if they don't have a background in the field.
  • Comfortable with cloud-based tools like BigQuery and SQLMesh, or willing to quickly learn them for data pipeline work.

📝 Tips for Applying to 1KOMMA5°

1

Highlight any project or coursework involving time-series forecasting, especially for energy or weather data—even if it's a class project.

2

Showcase your ability to work with large datasets in Python by linking to a GitHub repo with clean, well-documented code.

3

Mention any experience with cloud platforms (GCP, AWS) or data pipeline tools (dbt, Airflow) to stand out.

4

Tailor your cover letter to mention why you care about climate tech or renewable energy—1KOMMA5° values mission-driven candidates.

5

If you have experience with SQLMesh or BigQuery, explicitly call it out; if not, express enthusiasm to learn.

✉️ What to Emphasize in Your Cover Letter

['Your passion for using data science to tackle climate change and support renewable energy adoption.', "Specific examples of time-series forecasting projects you've done, even if academic, and the impact they had.", 'Your proficiency with Python and ML libraries, and your ability to build robust, maintainable code.', 'Your eagerness to work in a fast-paced startup environment and contribute to production systems.']

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Read about 1KOMMA5°'s product ecosystem: their energy management system, Heartbeat AI, and how they integrate solar, storage, and EV charging.
  • → Look into their blog or news articles to understand their recent growth, funding, and partnerships in the German energy market.
  • → Familiarize yourself with the concept of 'prosumers' and how forecasting helps balance local energy grids.
  • → Check out their tech stack mentions: SQLMesh, BigQuery, and Python—understand how these tools are used in data engineering.

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Walk us through a time-series forecasting project you've worked on—what data did you use, what model, and how did you evaluate it?
2 How would you handle missing data or anomalies in a real-time energy forecasting pipeline?
3 Explain the difference between ARIMA and Prophet for forecasting, and when you'd use each.
4 How would you design a data pipeline to transform raw energy meter data into features for a forecasting model?
5 What is your understanding of the energy transition, and how do you see data science accelerating it?
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⚠️ Common Mistakes to Avoid

  • Applying with a generic cover letter that doesn't mention energy or forecasting—show you read the job description.
  • Overemphasizing deep learning without showing understanding of simpler, interpretable models—startups need practical solutions.
  • Neglecting to mention your availability as a working student (part-time hours) and your ability to work remotely in Germany.

📅 Application Timeline

This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.

Typical hiring timeline:

1

Application Review

1-2 weeks

2

Initial Screening

Phone call or written assessment

3

Interviews

1-2 rounds, usually virtual

✓

Offer

Congratulations!

Ready to Apply?

Good luck with your application to 1KOMMA5°!

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