Senior Decision Scientist, Core Experiences
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Life360Consumer technology
Location: Remote, USA. All positions, unless otherwise specified, can be performed remotely (within the US) regardless of any specified location above.Full-TimeSenior
Salary106,500 - 195,500 USD per year
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Job Details
- Experience
- 5+ years in an analytics, data science, or decision science role at a consumer tech company
- Required Skills
- PythonSQLA/B testingdbtR
Requirements
- Have 5+ years of experience in an analytics, data science, or decision science role at a consumer tech company.
- Have an advanced degree in a quantitative field, such as economics, statistics, quantitative social science, or operations research, or equivalent practical experience.
- Demonstrate applied causal inference experience, such as difference-in-differences, instrumental variables, regression discontinuity, synthetic controls, or propensity score matching.
- Show a track record of influencing product or business strategy through data, with specific examples of cross-functional impact.
- Have experience with experimentation platforms such as Statsig, Optimizely, or similar.
- Be proficient in SQL and Python or R for statistical analysis.
- Use AI tooling such as Claude Code or equivalent as a development partner, critically reviewing outputs and delegating discrete tasks.
- Structure ambiguous problems before choosing analytical tools.
- Take ownership from framing the question through delivering recommendations and tracking their impact.
- Preferred: be familiar with growth experimentation design in networked or social contexts.
- Preferred: have experience building dbt models or contributing to analytics engineering workflows.
- Preferred: have experience with retention analytics or subscription and freemium business models.
Responsibilities
- Partner with product, engineering, marketing, and finance teams on product and business decisions.
- Present clear analytical narratives and recommendations to leadership and working teams.
- Apply A/B testing, analytics, and causal inference to establish what causes observed outcomes.
- Work with engineering to implement event instrumentation.
- Translate analytical insights into product actions with Product.
- Develop and implement metrics strategies grounded in users’ product experiences.
- Explain why users behave as they do by connecting measurement to user motivation.
- Use coding agents and automated analysis, and contribute to the AI-native analytics stack.
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