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

Vanguard is not offering visa sponsorship for this position.

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.