SB Energy needs a Senior ML Engineer to develop and maintain optimization models for solar and storage projects, improve trading strategy performance, and enhance energy forecasting to optimize the profitability of their renewable energy portfolio.
Requirements
- Proficient with Python.
- Strong working experience with Python data science and machine learning packages, such as NumPy, Pandas, Matplotlib, Plotly, Scipy, stats model, XGBoost, Scikit-Learn, Keras, TensorFlow, PyTorch, pyomo, OR-tools etc.
- Familiar with the technical aspects of solar + storage modeling for CAISO and ERCOT markets, power systems, including power flow modeling, basis, congestion, transmission network, and the impact of generation (solar, wind, gas, coal, etc.) and load on LMPs.
- Expertise in optimization, multivariate time series forecasting, stochastic modeling and optimization, and Monte Carlo simulations.
- Experience in supporting energy trading, congestion management, and battery analytics.
- ML engineering experience
- Experience in the energy Market
Responsibilities
- Develop, maintain, and improve SB Energy’s Solar+ Storage trading tool using machine learning pipelines for CAISO and ERCOT markets.
- Implement data science and ML engineering best practices in automated algorithmic trading, backtesting, and benchmarking use cases.
- Collaborate with cross-functional teams to provide backend support for intelligent EMS (energy management system) optimization in SB Energy’s test facility center.
- Working with the engineering and power marketing team to develop data center hourly shaped power modeling product using renewable energy resources.
- Develop AI agents to analyze trading strategy performance, backtest strategies, and adjust bids/offers based on trade results for solar+storage (DART, PTP, Ancillary Services), and 24/7 modeling strategies.
- Collaborate with Portfolio management, power marketing, and Risk team to develop Solar + Storage trading strategies (for resource adequacy (RA), Merchant arbitrage strategy, and Ancillary services), and real-time backtesting between strategies for optimal revenue along with the understanding of the risk-reward of various strategies.
- Explore solutions for improving trading strategy performance, energy/LMP forecasting performance, benchmarking competition, and backtesting trading/hedging strategies.
Other
- Bachelor's Degree in a quantitative discipline (e.g., Electrical and Computer Engineering, Computer Science, Statistics, Math, Operations research or related field).
- 5-8 years of ML engineering experience with preferably 2-3 years in the energy Market.
- Location: Houston, TX, San Francisco Bay Area, CA, Denver, CO, or remote option available. The position may require up to 10% domestic travel.
