Principal Data Scientist

Principal Data ScientistLove what you do and leave your markHybrid ways of working - Sydney or Melbourne locationStrategic & Technical Leadership: Set the Data Science and AI technical direction across agile squads within the Retail Merchandising Product pillar.Cutting-Edge AI at Retail Scale: Design, build and scale forecasting, optimisation, causal inference, and agentic AI solutions that directly shape pricing, promotion, range, and space decisions.As a Principal Data Scientist in our Data & AI (D&AI) team, you will provide strategic and technical leadership for Data Science and AI in retail Merchandising. Working across agile squads, you will set technical direction, tackle complex problems and guide the delivery of high-impact initiatives in a collaborative environment that encourages continuous learning and innovation. Partnering closely with business stakeholders and technical teams, you will combine deep scientific expertise, commercial judgement and hands-on technical leadership to develop scalable, production-ready data science and AI solutions. You will evaluate and apply established and emerging approaches to strengthen our capabilities, improve how our Merchandising teams operate and make decisions, and deliver measurable business value.Sound good? Read on.Here is a taster of what you can expect in this role:Strategic and Technical Leadership: Set the technical direction for Data Science and AI in the Retail Merchandising Product pillar, partnering with Product, Engineering and business leaders to shape the technical roadmap. Apply systems thinking to manage cross-squad reusable assets, guide architecture, modelling and evaluation decisions.Merchandising AI Solutions: Design, build and scale forecasting, clustering, optimisation and agentic AI solutions for promotion, pricing, range and space optimisation. Translate model outputs into actionable recommendations and operational workflows that improve category management decisions and commercial outcomes.Opportunity Identification and Innovation: Proactively identify and shape high-impact Data Science and AI opportunities, anticipate delivery risks, and validate new approaches against business value, feasibility and scalability.Scientific Rigour and Model Lifecycle: Design experiments and apply causal inference methods where appropriate to test hypotheses and validate incremental business impact. Provide technical oversight across the model lifecycle, from problem framing and development to deployment, monitoring and continuous improvement, setting best practice and standards .Cross-Functional Delivery: Partner closely with various technical teams to frame business problems, resolve technical trade-offs and deliver scalable, production-ready AI products. Remain hands-on with the most complex technical challenges and establish approaches that the wider team can utilise.Stakeholder Communication and Influence: Communicate complex analytical findings, trade-offs, and model insights clearly to both technical and non-technical audiences so data-driven decisions are trusted and adopted.