VP, Product, Growth Experimentation
New
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Life360Consumer mobile technology
All positions, unless otherwise specified, can be performed remotely (within the US and Canada) regardless of any specified location above.Full-TimeVp
Salary not disclosed
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Job Details
- Experience
- 15+ years in consumer products; 10+ years of managing high performing teams.
- Required Skills
- A/B testing
Requirements
- Bring 15+ years of experience in consumer products.
- Have deep, hands-on ownership of growth and experimentation at scale.
- Bring 10+ years of experience managing high-performing teams that deliver outcomes.
- Demonstrate strategic ownership in ambiguous problem spaces, including prioritization under constrained capacity and turning strategy into shippable milestones.
- Design and defend metric frameworks, including primary metrics, driver metrics, and guardrails.
- Demonstrate deep experiment-design fluency, including power, minimum detectable effect, guardrail metrics, A/B/n testing, multi-armed bandits, holdouts, and sequential-testing pitfalls.
- Understand interference, network effects, novelty effects, and the distinction between correlation and causation.
- Show a track record of moving activation, engagement, or retention through a portfolio of experiments.
- Think in funnels and cohorts and distinguish incremental growth from vanity metrics or feature mix shifts.
- Lead cross-functionally with Engineering, Design, Data Science, and Analytics.
- Bring mobile-first consumer fluency, including experimentation and instrumentation across iOS, Android, and international markets.
- Use AI substantively in research, analysis, experiment design, prototyping, drafting, and readouts; review and take accountability for AI-generated work.
- Build reusable AI prompts and workflows, share effective practices, and stay current with relevant tools.
Responsibilities
- Drive outcomes across the full funnel with a high-velocity experimentation program.
- Build hypothesis trees across acquisition, activation, onboarding, engagement, and retention, and prioritize experimentation capacity.
- Choose appropriate methods, including A/B/n tests, multi-armed bandits, and long-term holdouts.
- Partner with Data Science and Engineering to improve the experimentation platform, instrumentation, playbooks, templates, and standards.
- Produce clear experiment readouts, make ship, iterate, or kill decisions, and maintain a durable record of learnings.
- Protect member trust by ensuring experiments create genuine member value.
- Use AI across hypothesis generation, analysis, readouts, and experiment delivery, and build reusable AI workflows.
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