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.

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.

Senior Decision Scientist – Experimentation, Decision Enablement

Key skills