Quantitative Power Analyst (Data Scientist) Short Term Intraday Stack Models
Power Trading
As they scale from 3.5 GW to 7 GW of generation and add 500 MW of battery storage, they're building out their data science capability on the trading floor.
We are seeking a highly motivated Quantitative Power Analyst to join our trading team. This role is focused on short-term power markets and sits at the intersection of trading, quantitative analysis, and data-driven modelling.
Developing algorithms for asset optimisation in short-term power markets.
Applying short-term power trading expertise to drive trading desk performance.
Building and improving short-term pricing models (e.g., Take ownership over existing models, and building new short-term intraday stack models
Own and enhance short-term stack forecasting and dispatch models
Developing algorithms for optimising assets in the short-term power market, including dispatch/stack models used for pricing and trading decisions
Working directly with the trading desk to support short-term power trading strategies and decisions, ensuring models are commercially relevant and impactful.
Work alongside data engineers to deploy production-grade code
Mentor others and help embed data science best practices across the team
Masters degree in computer science, mathematics, engineering, physics, machine learning, or a related field. Proficiency in Python and ability to write clean, production-quality code.
~5+ years of relevant experience in short-term power trading, quant analysis, or algorithmic modelling.
~ Strong experience in short-term power trading, with direct impact on trading desk decisions.
~ Hands-on expertise in developing algorithms for asset optimisation in the short-term power market.
~ Proven experience with stack/dispatch modelling and short-term pricing techniques.
~ Experience in power markets, with knowledge of financial markets and trading concepts
~ Experience with back testing techniques appropriate to financial market applications
~ Experience exploring and extracting insights from heterogeneous multi-dimensional data sets, and presenting complex data visually
~ Time series modelling (both machine-learning and econometric approaches)
