At Habitat Energy we bring together exceptionally talented and passionate people in the domains of data science, software engineering and energy trading alongside an in-depth understanding of flexible energy assets. Our aim is to maximize the value of renewable assets, specifically, grid-scale battery storage, so they are attractive investments, are deployed at scale and enable the energy transition.

We are looking for smart, motivated people to join our team who share our belief that we can outperform the energy sector dinosaurs, have a positive impact on the planet and have fun doing it together.

We have a vacancy for a Data Scientist to join our team.

You will be helping to build our forecasting and algorithmic trading platform in the US markets:

• Build machine learning models to forecast demand, market prices, and participant behaviour.
• Build decision-making "agents" to act autonomously in power markets
• Bring an experimental mindset, prototyping and developing with the right model for the job.

We’re looking for someone who is a great fit for our company. We want people who take accountability, build trust and are innovative. We encourage you to apply, even if you may not meet every requirement in this posting. Furthermore, we value diversity and our environment is supportive, challenging and focused on the consistent delivery of high-quality, meaningful work.

Requirements:

• You are fluent in Python and its wider numerical ecosystem (Pandas, NumPy, Scikit-learn, etc.)
• You have a passion for time-series forecasting and a desire to apply that within the energy sector
• You have experience building algorithmic decision tools (e.g. mixed-integer linear programming)
• You have exposure to energy markets or battery systems modelling
• You have experience applying the data science toolset to time-series problems.
• You have at least 3 years of work/educational experience developing and productionizing real-time projects in Machine Learning and Data Science
• You have a BA/B.Sc. degree in Computer Science, Machine Learning or related technical field

Extra credit if you can demonstrate:

• MS or PhD degree in Computer Science, Machine Learning, or related technical field
• Knowledge in Bayesian methods, causal inference, dynamical systems, and/or reinforcement learning
• Knowledge of relational and/or non-relational databases (eg, SQL), and SQLAlchemy
• Full-stack/machine learning engineering experience
• Version control code (Git) including dev, test and production environments
• AWS, terraform, continuous integration, monitoring and alerting

In return, we’ll give you a competitive salary, flexible working arrangements and a lot of personal development opportunities.

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