We are building a core data science hub in Austin and require a Lead Data Scientist to lead the forecasting efforts for one of our clients in the U.S. This is a unique opportunity to drive forecasting innovation in power markets, leading a high-impact team working at scale. The successful candidate will bring demonstrable leadership and communication skills, deep technical expertise, and a strong understanding of energy markets and grid operations.

What You'll Do

Leadership & Team Development

• Lead and mentor a growing team of data scientists, fostering a culture of excellence, ownership, and continuous improvement.

Scalable Forecasting Systems

• Design, build, and deploy scalable probabilistic forecasting solutions to support real-time trading and operational decision-making.

Market-Focused Innovation

• Leverage deep domain knowledge in U.S. power markets (e.g., ERCOT, PJM, CAISO) to design forecasting solutions that align with market dynamics and strategic priorities.

Cross-Functional Collaboration

• Work closely with engineering, trading, and commercial teams to translate business objectives into forecasting models that generate tangible value.

Production-Grade ML

• Own the end-to-end lifecycle of forecasting models: development, deployment, monitoring, and improvement. Strong partnership with MLOps and DevOps teams is essential.

Thought Leadership

• Drive internal research, engage with academic and industry thought leaders, and help shape forecasting strategy at the organizational level.

To be successful in this role you will have:

• Proven technical leadership experience in data science, ideally leading teams solving complex, high-scale forecasting problems.
• Strong communication and interpersonal skills—able to work across technical and non-technical teams with clarity and influence.
• Deep expertise in probabilistic modeling, time series forecasting, and uncertainty quantification.
• In-depth experience with large-scale forecasting systems and working with high-frequency, high-volume data streams.
• Practical knowledge of U.S. power markets (especially ERCOT, PJM, CAISO), grid operations, pricing dynamics, and market design.
• Advanced programming skills in Python and fluency with modern data science libraries (e.g., PyTorch, Scikit-learn, Polars, NumPy).
• Demonstrated ability to take models from concept to production, including experience with MLOps, model monitoring, and CI/CD pipelines.
• Background in working with cloud infrastructure (e.g., AWS) and deploying models at scale.
• Experience forecasting load, renewable generation, or prices in U.S. ISO/RTO markets.
• Expertise in generative modeling, Bayesian methods, or causal inference.
• Familiarity with optimal power flow (OPF) or market simulation models.
• Demonstrated impact in feature engineering and predictive signal development.
• Knowledge of GenAI tools and techniques for advanced forecasting workflows.

Location Requirement:

This role must be based in Austin, Texas. We are building a core data science hub in Austin and require close collaboration with other teams on the ground.