Senior Product Manager – Experimentation & Optimization
New
Based in the United StatesFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- AgileData AnalysisProduct ManagementA/B testing
Requirements
- Bachelor’s degree in Business, Computer Science, Engineering, Data Science, or a related field (Master’s preferred).
- 5+ years of Product Management experience with a strong focus on experimentation, optimization, and data-driven product development.
- Hands-on experience with experimentation platforms such as Optimizely, Adobe Target, Statsig, or equivalent tools.
- Strong understanding of experimentation methodologies, including A/B testing, multivariate testing, holdout testing, and sequential testing.
- Solid grasp of statistical principles such as significance testing, sample size estimation, and bias/variance trade-offs.
- Proven ability to analyze complex datasets and translate findings into clear, strategic product decisions.
- Experience building and scaling experimentation programs, frameworks, and governance processes in large organizations.
- Strong communication, storytelling, and stakeholder management skills across technical and non-technical audiences.
- Experience working in Agile product environments with cross-functional teams.
Responsibilities
- Define, lead, and continuously refine the enterprise experimentation strategy across digital products and platforms.
- Build and manage experimentation roadmaps aligned with product, customer, and business objectives.
- Design, execute, and analyze experiments (A/B, multivariate, holdout, sequential testing) to validate hypotheses and improve user experience.
- Establish scalable experimentation frameworks, governance models, and best practices across multiple product teams.
- Perform deep analysis of experiment outcomes, translating data insights into actionable product recommendations.
- Partner with Engineering, Data Science, AI/ML, UX, Analytics, and Product teams to design, implement, and evaluate experiments.
- Ensure statistical rigor in experimentation design, including significance testing, sample sizing, and bias evaluation.
- Communicate experiment results and insights to stakeholders, influencing product strategy and prioritization decisions.
- Drive a culture of experimentation, continuous learning, and data-driven decision-making across the organization.
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