Statistician and Forecasting Analyst

About the role

The Actuarial Analyst supports the Energy Markets function serving the Australian energy market by applying actuarial methods, statistical techniques, and quantitative analysis to assess financial uncertainty, evaluate market risks, and support commercial decision-making. The role involves developing analytical models, analysing historical and forecast market data, conducting scenario and sensitivity analysis, and translating complex quantitative results into actionable insights for 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.
• Apply actuarial techniques, statistical methods, and quantitative models to evaluate financial uncertainty, market volatility, and potential portfolio exposures.
• Perform statistical analysis, time-series modelling, and forecasting to identify market trends, assess variability, and support financial and commercial planning.
• Conduct scenario analysis, sensitivity testing, and stress testing to evaluate the potential impact of changing market conditions on portfolio performance and financial outcomes.
• Analyse historical market movements and relevant datasets to identify trends, correlations, risk drivers, and potential sources of financial uncertainty.
• Assess hedge positions, portfolio exposures, and financial performance across electricity, gas, and environmental certificates.
• Support the valuation and quantitative assessment of energy contracts and financial instruments, including swaps, caps, power purchase agreements, and environmental certificate positions.
• Develop and maintain Python-based analytical models and SQL processes for data preparation, statistical analysis, forecasting, and reporting automation.
• Build and maintain Power BI dashboards and analytical reports to present model outputs, market trends, financial indicators, and key findings.
• Validate model assumptions, test calculations, investigate data anomalies, and document methodologies to ensure analytical accuracy and reliability.

About you

• Bachelor's degree in Actuarial Science, Mathematics, Applied Mathematics, Statistics, Financial Mathematics, Data Science, Physics, Quantitative Economics, or another demonstrably quantitative discipline.
• Graduates of Engineering, Computer Science, or Information Technology programs may also be considered if they can demonstrate strong mathematical, statistical, and quantitative analytical capabilities.
• Typically, 4–8 years of relevant experience in actuarial analysis, statistical modelling, quantitative analysis, financial modelling, forecasting, risk analytics, market analytics, or related analytical functions.
• Strong understanding of actuarial principles, probability theory, statistical inference, financial mathematics, and quantitative modelling.
• Strong Python programming skills, including experience in statistical computing, time-series analysis, forecasting, and automated analytical processes.
• Proficiency in SQL, including the ability to independently write and optimise complex queries for structured datasets.
• Advanced Microsoft Excel skills, particularly in quantitative calculations, financial analysis, statistical modelling, and data validation.
• Proficiency in Power BI or an equivalent business-intelligence platform for data visualisation and analytical reporting.
• Strong numerical reasoning and problem-solving skills, with the ability to analyse complex datasets, test assumptions, and interpret quantitative results.
• Ability to communicate statistical findings, modelling assumptions, and financial implications clearly to technical and commercial stakeholders.

Benefits

• 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 actuarial analysis, quantitative modelling, financial uncertainty, wholesale energy-market dynamics, and portfolio risk assessment.
• Opportunities to develop Python programming, SQL data management, statistical modelling, forecasting, and business-intelligence reporting capabilities.
• The opportunity to apply actuarial and quantitative techniques to real-world energy-market challenges and support data-driven commercial decisions.