Application Guide

How to Apply for Data Scientist

at Arenko

🏢 About Arenko

Arenko is at the forefront of the clean energy transition, using AI-powered software to optimize battery storage and trading in real-time. Their mission to enable a zero-carbon grid is both impactful and technically challenging, making it an exciting place for data scientists who want to apply their skills to climate change.

About This Role

As a Data Scientist at Arenko, you'll analyze massive streams of market and operational data to uncover trading opportunities and improve the performance of their energy storage optimiser. Your work directly influences revenue and grid efficiency, with models deployed into production to drive real-world decisions.

💡 A Day in the Life

You might start by reviewing overnight market data and model performance dashboards, then join a stand-up with the trading team to discuss recent signals. Later, you'll prototype a new feature for the optimiser, run historical simulations, and present findings to stakeholders—balancing deep analysis with fast iteration to improve real-time decisions.

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Interview Prep

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🎯 Who Arenko Is Looking For

  • A quantitative thinker with 2+ years of experience in data science or quantitative analysis, ideally in energy trading, finance, or a similarly fast-paced domain.
  • Highly proficient in Python, with a track record of writing production-ready, maintainable code and deploying ML models.
  • Strong foundation in statistics and probability, comfortable designing experiments and A/B tests to validate trading strategies.
  • Passionate about clean energy and motivated by the challenge of optimizing complex systems under uncertainty.

📝 Tips for Applying to Arenko

1

Highlight any experience with time series forecasting, optimization, or reinforcement learning, as these are directly relevant to trading and optimiser behavior.

2

Showcase your ability to translate data insights into actionable trading strategies—include examples of how your analysis led to measurable improvements.

3

Mention any familiarity with energy markets (e.g., day-ahead, intraday, balancing) or battery storage systems, even if from academic projects.

4

In your CV and cover letter, emphasize your Python coding standards and experience with version control, testing, and deployment (e.g., Docker, CI/CD).

5

Tailor your portfolio or GitHub to include projects that involve large datasets, simulation, or decision-making under uncertainty.

✉️ What to Emphasize in Your Cover Letter

['Your passion for using data science to accelerate the clean energy transition and reduce carbon emissions.', "Specific examples of how you've developed and deployed ML models that improved decision-making or revenue.", 'Your comfort with uncertainty and complex systems, and how you approach designing experiments in real-world settings.', "Why Arenko's mission and technology excite you, and how your skills align with their focus on trading and optimization."]

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Read about the UK electricity market structure (e.g., wholesale, balancing mechanism, ancillary services) and how battery storage participates.
  • → Study Arenko's blog or press releases to understand their technology stack and recent projects.
  • → Familiarize yourself with common metrics for trading performance (e.g., P&L, Sharpe ratio, hit rate) and how they apply to energy storage.
  • → Look into the company's competitors (e.g., Fluence, Tesla) and how Arenko differentiates itself with AI.

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Walk through a time series forecasting project: how you handled seasonality, trends, and uncertainty.
2 How would you design an experiment to test a new trading strategy in a simulated environment?
3 Explain a gradient boosting model you've deployed; how did you ensure it was production-ready?
4 Describe a situation where you had to make a decision with incomplete data; what approach did you take?
5 What are the key challenges in optimizing battery storage trading, and how would you address them?
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⚠️ Common Mistakes to Avoid

  • Sending a generic application that doesn't mention energy or trading—show specific interest in the domain.
  • Overemphasizing deep learning without connecting it to the role's focus on optimization and statistics.
  • Failing to demonstrate production experience; be ready to discuss how you've moved models from notebooks to live systems.

📅 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!

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Good luck with your application to Arenko!

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