We’re looking for a highly analytical and numbers drive Data Scientist to join our Melbourne trading floor and contribute directly to the models that drive commercial decisions across our battery, wind, and solar portfolio. You’ll build and maintain forecasting models, work with real NEM market data daily, and apply AI and machine learning tools as a core part of how you work. This role suits someone with a strong forecasting foundation who is eager to deepen their expertise in energy markets, working alongside experienced traders. You’ll be on-site in our Brighton office, embedded in the daily rhythm of the trading team, close to the action, with direct visibility of how your work translates into commercial outcomes. A core part of this opportunity is working daily alongside some of Australia’s most talented energy traders, helping to develop the trading tools and strategies that directly improve trading performance and revenues. Much of this work happens in real time, meaning your contributions will have an immediate and measurable impact on the desk’s commercial outcomes. About the Company Equis through one of its subsidiary companies is one of Australia’s leading renewable energy developers, owning and operating one of the nation’s largest battery energy storage system (BESS) portfolios. Equis is driving Australia’s transition to a clean energy future through an ambitious portfolio of large-scale battery, and wind assets across the NEM. Our flagship asset is the Melbourne Renewable Energy Hub (MREH) in Plumpton, Victoria, one of the largest battery projects in the world, delivering 600 MW / 1,600 MWh of storage, co‑owned with SEC Victoria. Beyond MREH, Equis is developing a national BESS portfolio spanning New South Wales, South Australia, and Queensland, alongside an onshore wind portfolio across Queensland, Tasmania, and New South Wales. Our portfolio has expanded at an average of 174 MW per month since late 2021, and we have operations and teams across Australia, Singapore, Japan, and South Korea. Job Requirements Skills & Experience Strong experience in data science, quantitative analysis, or a closely related field Strong understanding of time‑series analysis, statistical modelling, and model evaluation methods Proven track record building forecasting or predictive models and deploying them in production or semi‑production environments Strong Python skills across the data science stack: pandas, NumPy, scikit‑learn, and at least one deep learning framework (PyTorch or TensorFlow) Proficient use of AI tools (Claude or similar) Proficiency in SQL and experience working with cloud data platforms (AWS, Azure, or GCP) Experience working with electricity market data, AEMO NEM data (dispatch prices, FCAS, generation, bids/offers) is a significant advantage Familiarity with energy trading concepts: spot markets, ancillary services, battery dispatch, renewable generation Exposure to weather‑driven forecasting and Numerical Weather Prediction data integration Experience with MLOps practices: model versioning, monitoring, and automated pipeline deployment Education Bachelor’s degree or higher in a quantitative discipline: Mathematics, Statistics, Computer Science, Engineering, Physics, or Economics Postgraduate qualification in data science or machine learning is a plus but not required Analytical rigour, methodical approach to model development, testing, and validation Commercial awareness, ability to connect modelling work to trading outcomes and revenue impact Curiosity and initiative, proactively seeks out new techniques, data sources, and market insights Communication, able to explain complex models clearly to traders and non‑technical stakeholders Teamwork, thrives in a collaborative, office‑based team environment Analyse and interrogate NEM behaviour and asset performance to investigate potential trading opportunities Develop short‑term electricity price forecasting and generation forecasting models for the NEM Support battery dispatch optimisation modelling, incorporating energy arbitrage and FCAS revenue streams Back‑test and validate model performance against live market outcomes and contribute to ongoing model improvement Apply ML techniques and AI more generally to enhance analytics, forecasting and modelling responsibilities Use AI‑assisted tools to accelerate analysis and reporting Stay current with advances in forecasting and AI/ML and bring relevant new techniques to the team Work with data pipelines ingesting AEMO market data, SCADA telemetry, and weather feeds Ensure model outputs are accurate, reliable, and delivered to trading systems in a timely manner Maintain data quality standards and flag anomalies or market events that affect model inputs Work closely with traders on the floor to translate commercial questions into modelling problems Clearly communicate model outputs, assumptions, and limitations to both technical and non‑technical colleagues Contribute to team knowledge‑sharing, code reviews, and documentation What We Offer Competitive base salary plus performance bonus Full‑time office role in our Brighton office, you’ll be embedded in the action Direct mentorship from experienced energy traders and exposure to live trading decisions Strong learning opportunities and environment, exposure to live NEM trading across multiple jurisdictions Modern cloud‑native data stack, best‑in‑class tooling, no legacy systems Dedicated learning & development budget and support for industry certifications and conferences A genuine opportunity to grow into a senior role as the team and portfolio expand #J-18808-Ljbffr