Risk Modelling Analyst

About the role

The Risk Modelling Analyst supports the Energy Markets function serving the Australian energy market by developing, maintaining, and validating quantitative models used to assess market risk, portfolio exposure, and financial uncertainty. The role involves analysing historical and forecast market data, conducting scenario and sensitivity analysis, monitoring risk exposures, and automating analytical processes to support informed decision-making across the Energy Markets team.

Key responsibilities

• Collect, validate, and analyse historical and forecast energy market data, including electricity and gas prices, demand, generation, half-hourly load profiles, and hedge positions.
• Develop, maintain, and validate quantitative models to assess market risk, portfolio exposure, price volatility, and potential financial losses.
• Perform statistical analysis, time-series modelling, and quantitative assessments to identify market trends, risk drivers, and potential exposure to adverse market movements.
• Conduct scenario analysis, stress testing, and sensitivity analysis to evaluate the impact of changing market conditions on portfolio value and financial performance.
• Analyse hedge positions and portfolio exposures across electricity, gas, and environmental certificates, identifying material movements and emerging risk concentrations.
• Support the valuation and risk assessment of energy contracts and financial instruments, including swaps, caps, power purchase agreements, and environmental certificate positions.
• Develop and maintain Python and SQL processes for data preparation, model execution, validation, and automated risk reporting.
• Build and maintain Power BI dashboards and reporting tools to communicate risk metrics, model outputs, and portfolio movements to relevant stakeholders.
• Monitor model performance, investigate anomalies, validate assumptions, and document methodologies, limitations, and model changes.
• Prepare clear analytical reports and written commentary explaining quantitative findings, key risk drivers, model outcomes, and their potential financial implications.

About you

• Bachelor's degree in Actuarial Science, Mathematics, Applied Mathematics, Statistics, Computer Science, Data Science, Physics, Quantitative Economics, Financial Engineering, or another demonstrably quantitative discipline.
• Engineering graduates with strong mathematical, statistical, and analytical foundations are also encouraged to apply.
• Graduates of Information Technology or Information Systems programs may be considered if they can demonstrate equivalent quantitative training, programming proficiency, and strong analytical capabilities.
• Typically, 4–8 years of relevant experience in risk modelling, quantitative analysis, market risk, financial modelling, actuarial analysis, statistical modelling, energy analytics, or related analytical functions.
• Strong Python programming skills, including experience with statistical analysis, time-series data, quantitative modelling, and automated analytical processes.
• Proficiency in SQL, including the ability to independently write and optimise complex queries for large or structured datasets.
• Strong understanding of statistical methods, probability, forecasting techniques, model validation, and quantitative risk assessment.
• Advanced Microsoft Excel skills, particularly in quantitative analysis, data manipulation, and model development.
• Proficiency in Power BI or an equivalent business-intelligence platform for risk reporting and data visualisation.
• Ability to interpret complex datasets, identify material risk drivers, test assumptions, and explain quantitative results in clear business terms.

Why Join Us

• Six months of structured training in the Australian energy market.
• The opportunity to collaborate with an international Energy Markets team based in Sydney.
• Exposure to quantitative risk modelling, wholesale energy-market dynamics, portfolio risk, and financial analysis.
• Opportunities to develop advanced Python automation, SQL data management, statistical modelling, and business-intelligence reporting solutions.
• The opportunity to contribute to the development and continuous improvement of analytical models and risk management processes in a specialised energy-market environment.