About the position
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'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.
Responsibilities
- 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.
Requirements
- 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.
Nice-to-haves
- 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.
Benefits
- Competitive salary
- Flexible working arrangements
- Personal development opportunities
- Hybrid working model with at least 2 days in the office in Austin
