Optimization Research Scientist

Core Responsibilities

• Partner directly with senior business and investment stakeholders to uncover high-value opportunities, develop + iteratively refine hypotheses, and translate ambiguous questions into structured research problems.
• Formulate complex business and investment challenges as optimization problems, defining objectives, constraints, tradeoffs, decision variables, and measurable success criteria.
• Build and evaluate quantitative, statistical, machine learning, simulation, and optimization frameworks that support practical decision-making in real-world investment settings.
• Work with incomplete, noisy, fragmented, or evolving data to create usable research datasets, document assumptions, and assess the implications of data limitations.
• Design rigorous evaluation approaches, including out-of-sample testing, simulation, backtesting, sensitivity analysis, robustness testing, and constraint validation.
• Iterate closely with stakeholders, researchers, data scientists, and engineering partners to refine hypotheses, improve frameworks, and move promising research toward scalable implementation.
• Communicate findings, tradeoffs, assumptions, and recommendations clearly to business leaders, with a focus on decision impact and actionable next steps.

Qualifications:

• Experience in applied research, quantitative modeling, optimization, and machine learning, with the ability to independently drive ambiguous research efforts from problem discovery through recommendation.
• Strong ability to partner directly with senior business stakeholders to uncover high-value opportunities, develop hypotheses, and translate loosely defined questions into rigorous analytical or optimization approaches.
• Experience formulating complex business or investment problems in terms of objectives, constraints, tradeoffs, decision variables, and measurable outcomes.
• Strong experience building optimization models to support decision-making in real-world settings, experience with statistical, machine learning, and deep learning is a plus.
• Comfort working with incomplete, noisy, fragmented, or evolving data, including the ability to make pragmatic assumptions, document limitations, and keep research moving despite imperfect inputs.
• Experience designing and interpreting evaluation frameworks using out-of-sample testing, simulation, backtesting, sensitivity analysis, or robustness analysis.
• Proficiency in Python and comfort working in development environments such as SageMaker, Databricks, or similar platforms; familiarity with optimization libraries, solvers, or computational decision frameworks is valuable.
• Experience with quantitative finance, systematic workflows, or investment management problems is preferred; participation in the CFA program or related financial education is valuable.

Special Factors

Sponsorship

About Vanguard

At Vanguard, we don't just have a mission—we're on a mission.

To work for the long-term financial wellbeing of our clients. To lead through product and services that transform our clients' lives. To learn and develop our skills as individuals and as a team. From Malvern to Melbourne, our mission drives us forward and inspires us to be our best.

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.