As the Experimentation Senior Product Manager at ASOS, you'll own the experimentation capability end to end the standards experiments are designed to, the metrics they are measured against, the platform they run on, the data feeding their results, and the governance determining whether a result is trusted enough to act on. This role does not run individual experiments; it determines whether hundreds of experiments run by other people produce decisions the business can rely on.
ASOS runs experimentation at scale across Product, Engineering, Analytics, Trade and Data, and the volume of experiments isn't the constraint, the capability surrounding them is. When that capability is strong, experiments produce clear answers quickly and the business acts on them with confidence; when it's weak, teams run experiments that can't conclude, measure the wrong things, or produce results nobody trusts. This role owns the difference between those two states.
You'll define the vision and roadmap for how ASOS experiments, and be accountable for the quality and reliability of experimentation outcomes across the business, setting the standards experiments are held to and enforcing them through a weekly review cycle, owning the measurement foundations that determine whether results can be trusted, owning the experimentation platform and its strategy, and building the capability of the wider organisation through the Experimentation Forum, the Champion network, training and direct support to Product Managers.
The role is roughly 55% operational leadership — governance, programme delivery, stakeholder management, enablement, leadership reporting and vendor management — and 45% technical product leadership — measurement frameworks, statistical methodology, platform ownership, data and analytics integration, and AI and automation. The 45% is the distinguishing feature: a candidate who can run the programme but cannot challenge a metric definition or assess a Bayesian approach will deliver roughly half of what the role requires.
You'll collaborate with Product Analytics, Analytics Engineering, Engineering leaders and Trade partners to build a trusted, high-quality experimentation capability that drives faster and more confident product decisions.
Key Responsibilities
Define and own the experimentation vision, strategy and improvement roadmap, balancing governance, measurement, platform and enablement priorities against business goals.
Own Optimizely platform strategy, capability and health, and manage the vendor relationship including support, incident management and roadmap influence.
Work across Product, Engineering, Analytics, Trade and Data to keep experimentation aligned to company goals, communicating vision and outcomes clearly across the business.
Champion quasi-experimental methods (e.g. diff-in-diff, synthetic control, matched market tests) for scenarios where randomised testing isn't feasible, and build a playbook to guide their consistent application.