What are the responsibilities and job description for the Senior Data Scientist position at Habitat Energy?
Senior Data Scientist
Habitat Energy is a fast growing technology company focussed on the trading and algorithmic optimisation of energy storage and renewable assets around the world. Our mission is to deliver outstanding returns to our clients to increase the attractiveness of renewable energy globally and support the transition to a clean energy future. Our rapidly growing team of 90 people in Austin, TX, Oxford, UK, and Melbourne, Australia brings together exceptionally talented and passionate people in the domains of energy trading, data science, software engineering and renewable energy management.
We have a vacancy for a Data Scientist to join our Austin team based in Texas. This role will drive the development and implementation of advanced optimization and forecasting models, focusing on trading (DART), storage (BESS), portfolio, and congestion management across US power markets.
Your responsibilities will include:
Develop Forecasting Models and Decision Support Tools
- Create and maintain models for price forecasting, scenario analysis, and decision support, including machine learning models for energy market predictions and real-time forecasting.
Optimization Model Development and Maintenance
- Develop and maintain optimization models for energy storage and solar storage systems, maximizing portfolio performance and integrating market dynamics across ISOs.
Congestion Modeling and Management
- Build models to forecast and mitigate congestion risks, optimizing bidding strategies and asset dispatch in constrained markets.
Support Business Development
- Develop tools to backtest asset performance, collaborate with teams to prepare client materials, and provide insights for strategic initiatives.
Deployment and Integration
- Work with Software Engineering and DevOps teams to deploy scalable, robust forecasting and optimization models.
Innovation and Tool Optimization
- Use experimental approaches to enhance modeling techniques for forecasting, decision-making, congestion management, and portfolio optimization.
‘Must have’ skills and experience:
- You have at least 3 years of commercial experience developing and productionising real-time projects in Machine Learning and Data Science.
- Experience with short term US power markets
- Experience building algorithmic decision tools (e.g. mixed-integer linear programming, CVXPY).
- You have experience bringing your creative output to support development of quarterly product roadmaps.
- You are fluent in Python and its wider numerical ecosystem (Pandas, NumPy, Scikit-learn, Polars, etc.).
- You have a BA/BSc degree in Computer Science, Machine Learning, Electrical Engineering, or related technical field.
- Version control code (Git) including dev, test and production environments.
‘Nice to have’ skills and experience:
- Experience trading virtual and/or asset-backed positions in one or more major US Power Market (ideally ERCOT)
- Electricity & energy domain modelling (e.g. power systems modeling, power flow, security constrained unit commitment etc.)
- Full-stack / machine learning engineering / data engineering experience
- Experience with forecasting & time series problems
- Experience with data visualization and dashboarding technologies (e.g. plot.ly, Dash, Streamlit)
- Demonstrated track record of academic paper (NeurIPS, ICLR, ICML; and/or papers on power systems, optimization or other related topics)
- AWS, terraform, continuous integration, monitoring and alerting.
- MS or PhD degree in Computer Science, Machine Learning, or related technical field.
Ultimately we are looking for someone who is a great fit for our company so we encourage you to apply even if you may not meet every requirement in this posting. We value diversity and our environment is supportive, challenging and focused on the consistent delivery of high quality, meaningful work.
In return, we’ll give you a competitive salary, flexible working arrangements and a lot of personal development opportunities. We operate a hybrid working model with at least 2 days in our office in Austin.
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