Data Scientist, Specialist

Job Description

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This hybrid role (in office Tues-Wed-Thurs) is based in Melbourne, VIC. Our Chief Data & Analytics Office exists to empower Vanguard Australia to change the way Australians invest, by delivering trusted, accessible, and actionable data and analytics.

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Our Data Science team helps realise this vision by unlocking the power of advanced analytics, machine learning and AI to drive measurable business value across teams including Marketing, Digital, Client Services, Sales & Operations.

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This senior role will drive the design, development and delivery of production-ready machine learning solutions across Vanguard’s Investment, Superannuation and Financial Adviser business lines, working under the Head of Data Science to translate high-value business opportunities into scalable, governed and operationalised analytics products.

What you’ll do:\n

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• Lead the end-to-end delivery of data science and machine learning initiatives, reporting to the Head of Data Science to identify business opportunities, shape priorities, define success measures, manage delivery trade-offs and ensure work is aligned to business value.
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• Translate ambiguous business problems into structured analytical approaches, testable hypotheses and robust delivery plans, bringing clarity to complex stakeholder requests and cross-functional initiatives.
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• Design, develop, validate and optimise advanced machine learning & AI models, experimentation frameworks and decision systems using Python, SQL and modern ML libraries & frameworks, with strong attention to model performance, reliability, explainability, oversight and business impact.
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• Own the practical operationalisation of ML solutions, including feature engineering, reproducible pipelines, model packaging, scoring, deployment readiness, monitoring, observability, retraining approaches, documentation and handover into production support processes.
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• Work closely with Data Engineering, Technology, Governance and business teams to integrate ML outputs into consuming systems, improve data readiness, apply appropriate controls and ensure solutions are safe, scalable and maintainable.
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• Prepare and deliver clear, actionable recommendations to senior business and technical stakeholders, explaining modelling choices, trade-offs, assumptions, risks and expected value in practical business language.
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• Set and uplift team standards for model development, code quality, experiment design, reproducibility, monitoring and model governance, while coaching and mentoring more junior data scientists and analysts.
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What we are looking for:\n

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• At least 6 years’ experience in data science, machine learning, advanced analytics or a closely related field.
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• Significant hands-on experience delivering machine learning solutions across the full lifecycle, from problem framing, data preparation and model development through to deployment, monitoring and ongoing improvement.
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• Strong practical capability in Python, SQL and modern ML development practices, with experience working with large and complex datasets, version control, automated workflows and reproducible analytical environments.
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• Demonstrated experience with MLOps, including CI/CD concepts, model validation, model monitoring, drift detection, retraining approaches, documentation, governance and integration with downstream platforms or business processes.
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• Experience with modern cloud ML platforms such as AWS SageMaker, Azure Machine Learning, Google Vertex AI or MLflow.
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• Proven ability to lead delivery in a small, high-impact team: prioritising work, managing ambiguity, influencing stakeholders, making pragmatic delivery trade-offs and maintaining momentum without heavy supervision.
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• Strong communication and stakeholder management skills, with the ability to translate technical concepts into business language and build confidence with both senior leaders and delivery teams.
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• Undergraduate degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Analytics or a related quantitative field, or an equivalent combination of training and experience. A graduate degree preferred.
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• Experience working in a regulated industry such as financial services, superannuation, insurance, healthcare, defence or aerospace is a bonus.
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