We are looking for a Decision Scientist to join the Support Agent Tooling team within the Help Experience domain. This is a cross-functional team building and improving the systems, workflows, and analytical foundations that help support agents resolve member issues efficiently while maintaining a high-quality member experience.
This role is for someone who can independently own well-defined analyses from start to finish, contribute to product and operational decisions, and work closely with Product, Engineering, Operations, Analytics Engineering, and other Decision Scientists. The person should be comfortable turning business questions into structured analysis, interpreting results, and communicating clear recommendations to stakeholders.
Why this role matters
Support Agent Tooling directly affects both member experience and support efficiency. Better tooling can help agents resolve issues faster, reduce repeat contacts, improve consistency, and surface better feedback for product and operations teams.
We are building scalable ways to understand the quality of member-support interactions: what went well, what went wrong, where failures happened, and how tooling or workflows can improve outcomes. This person would help make those signals reliable, understandable, and useful for decision-making.
In this position, you’ll
- Enable Data-Driven Decisions: Develop and maintain relevant metrics and frameworks to track product performance, and continually explore opportunities within prioritised areas to communicate actionable analytical insights.
- Lead Experimentation: Design, run, and analyse experiments (A/B tests) to validate the value and business impact of product changes and tooling enhancements.
- Partner and Strategise: Collaborate cross-functionally to drive the process of setting team Key Results (KRs) and targets. You will challenge decisions and hypotheses to optimise our impact and maintain a strong member focus.
- Collaborate across teams, proactively aligning when work touches other areas of Help Experience, support operations, or data infrastructure.
- Build maintainable analytical workflows using SQL and Python, following good standards for code quality, reproducibility, documentation, and privacy.
