Senior Data Scientist, Habitat Energy
Forecasting Lead
2+ days a week in office
Battery storage and renewable energy optimisers
Be an early applicant
Battery storage and renewable energy optimisers
101-200 employees
Be an early applicant
2+ days a week in office
101-200 employees
Company mission
To deliver outstanding returns to our clients to increase the attractiveness of renewable energy globally and support the transition to a clean energy future.
Role
Who you are
- Expertise in statistical modelling, time series analysis, and machine learning for forecasting, with a focus on probabilistic methods and uncertainty quantification
- Strong understanding of power markets, including supply-demand dynamics, grid constraints, and pricing mechanisms
- Knowledge of power grid operations, transmission constraints, and reliability requirements
- Proven leadership in developing technical teams
- Based in or willing to relocate to Austin, working at least two days in the office
- Proficient in Python and data science tools (Polars, Pandas, NumPy, PyTorch, Scikit-learn)
- Experience with large-scale forecasting and scalable methodologies
- Skilled in communicating complex statistical concepts to technical and non-technical stakeholders
- Experience in MLOps, DevOps, and productionising machine learning solutions
- Strong collaboration with commercial, business development, and product teams
- Expertise in forecast evaluation, error analysis, and model improvement
- Proven ability to identify and validate predictive signals
- Experience with Git and managing team development workflows
Desirable
- Experience in ERCOT power market forecasting, including load, price, and renewable generation
- Deep understanding of ERCOT market design, nodal pricing, and settlement
- Familiarity with US ISOs (PJM, MISO, CAISO, SPP) and their market differences
- Knowledge of optimal power flow (OPF) for market modelling
- Expertise in distributed hyperparameter optimisation for large-scale models
- Strong background in probabilistic forecasting, generative time series modelling, and uncertainty calibration
- Experience analysing weather impacts on grid operations and market prices
- Knowledge of renewable integration challenges and market effects
- Proven track record of publishing or presenting forecasting methodologies
- Experience with AWS, automated model deployment, and monitoring
- Background in causal inference for forecasting
- Understanding of regulatory frameworks and market behaviour
- Success in developing high-value predictive features
What the job involves
- We’re hiring a Data Scientist in Austin, Texas, to lead advanced forecasting development and mentor a high-performing team
- This role combines probabilistic forecasting expertise with commercial acumen to drive value in power markets
- The successful candidate will collaborate across teams to scale forecasting systems and transform insights into operational advantage
- Lead and grow a forecasting team in Austin, fostering technical excellence and continuous learning. Provide mentorship, career guidance, and technical direction while establishing best practices
- Design and implement scalable forecasting systems, ensuring robust performance. Collaborate with MLOps and DevOps teams to productionize solutions and integrate new methodologies
- Maintain high forecast accuracy, calibration, and reliability. Develop evaluation frameworks, refine methodologies, and incorporate new data sources for continuous improvement
- Translate business needs into technical requirements. Work with trading, operations, and commercial teams to ensure forecasting solutions drive value. Present insights to senior management
- Lead research initiatives, explore new forecasting techniques, and collaborate with academic and industry partners to enhance methodologies
- Develop and execute the forecasting roadmap, aligning objectives with business goals. Manage resources and priorities for timely delivery
- Implement robust model validation and monitoring systems to detect forecast degradation. Ensure compliance with industry standards and best practices
Our take
With the world moving onto more sustainable energy, countries across the globe are aiming to harness the power of green energy alternatives. While sustainable energy production is on the rise, technology and finance need to work in tandem so that solutions are profitable and scalable.
Habitat Energy is combining the latest techniques in machine learning and artificial intelligence to provide customers with battery storage and renewable energy assets that deliver the best possible returns.
Following funding and partnerships, the company has invested capital into increasing its overseas portfolio and in the US market, expanding its product offerings into renewable and hybrid facilities. Already a market leader in the UK, the global expansion is already showing promise with a number of successful collaborations and projects already in the works. This is an exciting time for the company and will sure be one to watch in the coming years.
Kirsty
Company Specialist at Welcome to the Jungle
Company
Funding (2 rounds)
Jun 2020
$0.3m
GRANT
Apr 2019
$1.8m
GRANT
Total funding: $2.1m
Company HQ
City Centre, Oxford, UK
Articles
Leadership
Andrew Luers
(Co-Founder & CEO)Graduated from the University of Oxford with a Doctor of Philosophy in Physics. Also the Co-Founder of ProviderTrust where they serve as a Managing Partner.
Ben Irons
(Co-Founder & CSO)Previously an Executive Director and Board Member at Aurora Energy Research, Consulting Manager at McKinsey and Company and Strategy Director at Centrica Energy.
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