Staff Decision Scientist, Direct Revenue (AI Native)
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Life360Consumer technology
Remote, USA; Remote, Canada. All positions, unless otherwise specified, can be performed remotely (within the US and Canada).Full-TimeStaff
Salary118,500 - 218,500 USD per year
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Job Details
- Experience
- 8+ years in an analytics, data science, or decision science role at a consumer tech company.
- Required Skills
- PythonSQLR
Requirements
- Have 8+ years in an analytics, data science, or decision science role at a consumer tech company.
- Have direct experience with subscription monetization, including pricing, packaging, paywalls, conversion, or churn.
- Hold an advanced degree in a quantitative field or have equivalent practical experience.
- Demonstrate applied causal inference experience, such as difference-in-differences, instrumental variables, regression discontinuity, synthetic controls, or propensity score matching.
- Have a track record of partnering with executives and influencing major revenue or strategy decisions.
- Have experience designing and analyzing pricing or monetization experiments using Statsig, Optimizely, or similar platforms.
- Have experience formally or informally mentoring analysts or data scientists.
- Be proficient in SQL and Python/R for statistical analysis.
- Use Claude Code or an equivalent AI tool as a working partner, review outputs critically, and take responsibility for delivered analysis.
- Demonstrate executive presence, clear storytelling, stakeholder judgment, ownership, and willingness to push back with evidence.
Responsibilities
- Advise subscription executives on monetization opportunities and revenue decisions.
- Lead analysis of membership tiers, pricing, paywalls, trials, and upgrade and downgrade paths.
- Assess new revenue opportunities and bundles from initial sizing through test design and post-launch analysis.
- Guide market prioritization and adapt pricing and packaging using local willingness-to-pay, conversion, and retention data.
- Translate complex analysis into clear narratives and recommendations for decision-makers.
- Use A/B tests, pricing experiments, and causal inference to measure effects on conversion, retention, and LTV.
- Lead adoption of AI tools and agent workflows for monetization analysis.
- Set expectations with stakeholders and challenge questions or targets when evidence warrants.
- Mentor Decision Scientists in framing, causal rigor, and executive communication.
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