Data Scientist, Specialist

About Vanguard

More than 50 years ago, John C. Bogle had a vision to start an investment company that did things differently. A company with no external shareholders. Where all the profits were invested back into the business and used to lower costs. Evidently, it was as bold as it was brilliant. To this day, Vanguard Group still has no external shareholders. That means no share prices to protect, and no profits to generate for outside owners.

Today, Vanguard is one of the world’s largest investment management companies, serving more than 50 million investors worldwide. For more than 25 years Vanguard Australia has been supporting individual investors, financial advisers, and superannuation members to achieve their long-term financial goals.

Team and Opportunity

This hybrid role (in office Tues-Wed-Thurs) is based in Melbourne, VIC

• 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.
• Translate ambiguous business problems into structured analytical approaches, testable hypotheses and robust delivery plans, bringing clarity to complex stakeholder requests and cross-functional initiatives.
• 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.
• 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.
• 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.
• 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.
• 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.

What we are looking for

• At least 6 years’ experience in data science, machine learning, advanced analytics or a closely related field.
• 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.
• 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.
• 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.
• Experience with modern cloud ML platforms such as AWS SageMaker, Azure Machine Learning, Google Vertex AI or MLflow.
• 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.
• 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.
• 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.
• Experience working in a regulated industry such as financial services, superannuation, insurance, healthcare, defence or aerospace is a bonus.

Inclusion Statement

Vanguard’s continued commitment to diversity and inclusion is firmly rooted in our culture. Every decision we make to best serve our clients, crew (internally employees are referred to as crew), and communities is guided by one simple statement: “Do the right thing.”

We believe that a critical aspect of doing the right thing requires building diverse, inclusive, and highly effective teams of individuals who are as unique as the clients they serve. We empower our crew to contribute their distinct strengths to achieving Vanguard’s core purpose through our values.

When all crew members feel valued and included, our ability to collaborate and innovate is amplified, and we are united in delivering on Vanguard’s core purpose.

Our core purpose: To take a stand for all investors, to treat them fairly, and to give them the best chance for investment success.

How We Work

Vanguard has implemented a hybrid working model for the majority of our crew members, designed to capture the benefits of enhanced flexibility while enabling in-person learning, collaboration, and connection. We believe our mission-driven and highly collaborative culture is a critical enabler to support long-term client outcomes and enrich the employee experience.