Senior Decision Scientist (Experimentation & Decision Enablement)

Vanguard•Malvern, PA

5d•Hybrid

About The Position

Vanguard is seeking a Senior Decision Scientist to join their Experimentation & Decision Enablement team. This role involves designing and executing experiments to evaluate the impact of business and operational decisions, applying structured analytical approaches, and partnering with stakeholders to identify high-impact opportunities for testing and learning. The Senior Decision Scientist will input assumptions, parameters, and experimental findings into the decision product, interpret decision outputs, and translate them into clear, actionable guidance for business users. They will also communicate results, including implications, trade-offs, and uncertainty, to build trust and support decision-making. Collaboration with Data Scientists to ensure experimental insights are incorporated into the analytical engine and that decision outputs align with real-world workflows is also a key aspect of this role. The ideal candidate will balance statistical rigor with operational feasibility.

Requirements

Undergraduate degree (or equivalent experience) in a quantitative field; advanced degree preferred.
Experience designing, executing, and analyzing experiments or structured analytical studies in business environments.
Familiarity with causal inference concepts, including quasi-experimental approaches, or demonstrated ability to develop these skills.
Strong understanding of experimental design, statistical inference, and measurement frameworks.
Proficiency in Python, R, or similar tools for data analysis.
Ability to interpret analytical outputs and translate them into actionable business insights.
Strong communication and stakeholder management skills, with the ability to influence decision-making.
Comfort working in ambiguous, real-world environments where trade-offs between rigor and practicality must be managed.

Responsibilities

Design and execute experiments to evaluate the impact of business and operational decisions.
Apply structured analytical approaches, including quasi-experimental methods where appropriate, to generate evidence in real-world settings.
Partner with stakeholders to identify high-impact opportunities for testing and learning, prioritizing where evidence will reduce decision uncertainty.
Input assumptions, parameters, and experimental findings into the decision product to inform model outputs.
Interpret decision outputs generated by the analytical engine and translate them into clear, actionable guidance for business users.
Communicate results, including implications, trade-offs, and uncertainty, in a way that builds trust and supports decision-making.
Partner with Data Scientists to ensure experimental insights and learnings are incorporated into the evolution of the analytical engine.
Ensure decision outputs are aligned to real-world workflows and are usable within business planning and operational contexts.
Balance statistical rigor with operational feasibility when designing and executing experiments.