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.
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. Our Data Science team helps realise this vision by unlocking the power of advanced analytics, machine learning and AI to drive business value across teams including Marketing, Digital, Client Services, Sales & Operations.
Collaborate closely with internal stakeholders to understand business objectives, processes, and challenges, formulate testable hypotheses, and translate requirements into robust analytical solutions.
Design, develop, and optimise advanced queries and code using Python and SQL to access, clean, transform, and explore data and prepare large and complex datasets for advanced analytics and machine learning applications.
Conduct in-depth diagnostic, predictive, and prescriptive analytics leveraging state-of-the-art algorithms under the guidance of senior data scientists.
Support the implementation of experiments, statistical models, and machine learning solutions.
Prepare and deliver clear, actionable presentations and data-driven recommendations, effectively communicating complex analytical insights and their business implications to a broad audience, including non-technical stakeholders.
Stay up to date with the latest developments in data science, machine learning, AI, and analytics, actively participating in industry forums and continuous learning opportunities.
Contribute to the development and adoption of best practices, tools, and frameworks to improve analytic efficiency, model deployment, and reproducibility.
Experience with data wrangling (SQL, Python) and performing exploratory data analysis.
Experience utilising machine learning techniques.
Strong communicator who can translate technical concepts into business language.
Undergraduate degree in Science, Analytics, Mathematics, Computer Science or a related analytical field of study or equivalent combination of training and experience. Graduate degree preferred.
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.