Data Scientist

Why this role exists: The Data Scientist is a key member of the Advanced Analytics team, responsible for the end-to-end delivery of advanced analytics projects. The role partners with various business functions to understand requirements and develop the analytic methods and algorithms necessary to support data-driven decision-making across the organization. Where you will make a difference: In this role you will: Data Science and Model Development: Develop, test and improve statistical, machine learning and optimization models to solve priority business problems.

Apply appropriate data science techniques such as regression, classification, clustering, forecasting, segmentation, experimentation, optimization and recommendation models. Use programming tools such as Python, R and SQL to prepare analysis, build models and evaluate model performance. Select fit-for-purpose modelling approaches based on the business problem, data quality, interpretability needs and implementation pathway.

Document model assumptions, methodology, limitations and performance so outputs are transparent and reusable. Use Case Shaping and Analytical Problem Solving: Work with Analytics Business Partners, Business Analysis & Process Improvement roles and business stakeholders to understand the business problem, decision need and expected outcome. Help assess whether data science is the right approach for a business problem, or whether simpler analysis, reporting or process improvement is more appropriate.

Translate business questions into testable hypotheses, modelling approaches and success measures. Support opportunity sizing, scenario analysis and analytical design for priority initiatives and cross-functional squads. Provide technical input into feasibility, data requirements, delivery risks and expected model value.

Squad and Enterprise Support: Support squads, projects and enterprise initiatives by providing data science expertise where advanced modelling or analytical methods are required. Partner with Data Analysts to ensure model outputs are interpreted clearly and linked to business decisions. Partner with Analytics Engineers and Data Engineers to ensure the data required for modelling is available, fit for purpose and well understood.

Work with AI Engineers or technical teams where models need to be integrated, automated or scaled into business processes. Support transition of models into BAU with clear documentation, monitoring requirements and ownership arrangements. Model Quality, Decision Support and Responsible Use: Validate and monitor model performance using appropriate statistical, technical and business outcome measures.

Ensure models are explainable, reliable and fit for the decisions or business processes they support. Document model methodology, assumptions, limitations, risks and recommended use. Translate model outputs into clear insights, recommendations and practical decision support.

Use visualization and storytelling to explain patterns, trade-offs and expected business impact. Support business teams to understand how model outputs should be used, including where human judgment or review is required. Apply privacy, security, governance and responsible AI principles throughout the data science lifecycle.

Process & Continuous Improvement: Deliver projects using Agile methodologies, ensuring iterative value delivery and alignment with evolving business needs. Maintain a proactive problem-solving attitude, seeking to constantly improve the effectiveness of analytical models and workflows. Stay updated on industry trends and emerging technologies to keep Officeworks' analytical capabilities competitive.

Who you will be working with: Internal Delivery Teams: Data Engineers, Analytics Leads, and Business Analysts. Internal Partners: Senior leaders and teams across Finance, Merchandise, B2B, Supply Chain, Marketing, Property, Store Operations, and People. External Partners: Third-party analytical support providers.

What success looks like: Actionable Insights: Business functions are successfully making decisions driven by data insights and advanced algorithms. Model Performance: Statistical models and machine learning algorithms are accurately predicting or solving business use cases. Seamless Integration: Analytics products are successfully implemented and adopted within the target business functions.

Operational Quality: Solutions meet high quality-control standards through robust testing and QA processes. How you will lead: Individual Contributor: Lives our Officeworks values and behaviors Proactively contributes to a safe working environment, escalates appropriately if there are unsafe conditions or inappropriate behavior Operates in line with applicable Officeworks company policies and Code of Conduct Demonstrates a strong sense of personal accountability and curiosity to learn and develop Qualifications and work experience: Essential: Education: Bachelors degree in Mathematics, Applied Mathematics, Statistics, Physics, Computer Science, or a related field. Experience: 5+ years of experience in complex data science and analytics environments.

Technical Expertise: Deep knowledge of machine learning algorithms (GLM, Neural Networks, etc.) and proficiency in R, Python, and SQL. Platform Knowledge: Experience working within AWS compute and analytics platforms. Communication: Exceptional verbal and written communication skills with the ability to present complex data to non-technical stakeholders.

Preferred: Visualisation: Strong skills in using PowerBI, Tableau, or similar visualisation tools. Agile: Experience delivering data science solutions within an Agile framework. Retail Context: Experience applying data science within a large-scale retail or omnichannel environment.