Senior Product Manager - A/B Testing & Experimentation

Position : Senior Product Manager

100% Remote

JOB DESCRIPTION:

The Product Manager, A/B Testing & Experimentation is the primary owner of the A/B testing experimentation program for Reporting to the Head of Strategy, Operations & Growth within the Digital & eCommerce Product organization. This role drives a relentless experimentation cadence across product management, analytics, and product marketing. The PM exercises sharp prioritization to determine which tests to launch, accelerate, or kill, and translates data into actionable recommendations that improve digital experiences and business growth.
KEY RESPONSIBILITIES
Experimentation Program Leadership
Own and drive the A/B testing roadmap, prioritizing experiments that maximize customer digital experience and revenue impact.
Run a relentless experimentation cadence in partnership with other product managers and additional cross-functional teams.
Serve as the primary contact for all experiment requests; validate hypothesis, prioritize tests, triage, assess feasibility, and manage the experimentation pipeline.
Make sharp, data-backed prioritization calls on which tests to launch, pause, scale, or kill, balancing business value against engineering complexity.
Manage any winning tests at 100% on until ready to launch fully.
Test Design & Execution
Design and manage A/B, multivariate (MVT), and split-URL tests with rigorous statistical methodologies to ensure valid, actionable results.
Oversee the full testing lifecycle: from hypothesis definition and test specification through QA, deployment, monitoring, and post-test analysis.
Partner with product managers to ensure tests are properly implemented, instrumented, and deployed into production.
Define success metrics, KPIs, and guardrail metrics for every experiment.
Analytics, Measurement & Insights
Build and maintain measurement processes that translate performance insights into actionable growth recommendations for product, marketing, and business stakeholders.
Review post-test analysis with data science team, synthesizing quantitative and qualitative signals to produce clear, executive-ready recommendations.
Collaborate with analytics team to develop dashboards and reporting frameworks to track experiment performance, program velocity, and cumulative business impact.
Operate as a strong quantitative analyst, applying statistical techniques (significance testing, Bayesian analysis, segmentation) to separate signal from noise.
Stakeholder Communication & Documentation
Coordinate documentation of all experiment results, maintaining a centralized knowledge base to prevent duplication and accelerate organizational learning.
Communicate results and strategic implications clearly to diverse audiences, from engineers to senior business leaders and marketing teams.
Partner with analytics and product management to align on priorities and embed a culture of testing and evidence-based decision-making.

REQUIRED QUALIFICATIONS
5+ years of product management or digital optimization experience, with strong focus on A/B and multivariate testing programs
Proven track record of owning and scaling experimentation programs at high-traffic eCommerce or digital platforms
Expert proficiency in testing platforms (e.g., Optimizely, Adobe Target, VWO, Dynamic Yield, or equivalent)
Strong quantitative and statistical skills deep understanding of experiment design, sample sizing, significance testing, and bias mitigation
Experience collaborating cross-functionally with product management, analytics, and marketing teams
Demonstrated ability to manage a prioritized testing pipeline and make data-driven trade-off decisions
Excellent communication skills with the ability to translate complex data into business-ready insights
Hands-on experience with web analytics tools such as Google Analytics, or similar

PREFERRED QUALIFICATIONS
Experience in B2B eCommerce or life sciences / scientific instrumentation industries
Familiarity with product catalog structure
Proficiency in SQL, Python, or R for self-serve data analysis
Background in UX research or customer journey mapping to inform experiment hypothesis development
Experience with personalization and segmentation strategies in conjunction with A/B testing
Exposure to Bayesian experimentation frameworks
Ability to influence without authority across large, matrixed organizations
Experience presenting to stakeholders on digital growth initiatives