Senior Product Manager - Experimentation

ASOS · London, England, United Kingdom

  • Senior
  • Full-time
  • Posted 2026-09-18
  • Confirmed live on 25 September 2026

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Job description

Job Description
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.

• Define and enforce the experimentation standards ASOS works to, reviewing upcoming experiments for quality and readiness before launch through a weekly review cycle.

• Own the experimentation measurement strategy, defining primary, secondary and guardrail metrics and driving improvement in statistical validity and conclusive rates.

• Drive adoption of advanced methodologies including Bayesian sequential testing, multivariant testing and multi-armed bandits, partnering with Analytics teams and external experts.

• Own Optimizely platform strategy, capability and health, and manage the vendor relationship including support, incident management and roadmap influence.

• Design AI-enabled experimentation workflows and agents, with governance for AI-assisted experimentation agreed before tooling deploys.

• Partner with Product Analytics and Data to improve experiment data quality, driving ADE integration, metric certification and pre-launch measurement readiness gates.

• Define the standard experimentation workflow, launch gates and rollout decision frameworks that catch quality issues before they become expensive.

• Run the Experimentation Forum and Champion network, and support Product Managers directly on experiment planning, training and best practice.

• Define and track the KPIs measuring experimentation capability health, and report programme performance to Product Directors and senior leadership.

• 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.

• Develop frameworks to measure the financial impact of experiments, connecting experiment outcomes to commercial value and business KPIs.

Qualifications
About You

• Deep experience running experimentation or A/B testing programmes at scale across multiple teams, ideally in ecommerce or a consumer digital environment, with practitioner-level knowledge sufficient to credibly challenge a hypothesis, a metric definition or an experiment design.

• Hands-on expertise in frequentist and Bayesian approaches, sequential testing, multivariant testing and multi-armed bandits, sufficient to evaluate their suitability on merit and guide adoption.

• Expertise designing primary, seconda

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