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 dont just have a missionwere 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.