Lead Data Scientist

New

Life360Consumer technology

Remote, USA; Remote, Canada. All positions, unless otherwise specified, can be performed remotely (within the US and Canada).Full-TimeLead

Salary175,000 - 218,000 USD per year

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Job Details

Experience: 6+ years of experience scoping, building, and analyzing ML-powered systems, including models you have shipped to production.
Required Skills: PythonMachine LearningDatabricksscikit-learnPySpark

Have an advanced degree in a field that relies on sophisticated statistical analysis, or equivalent industry experience.
Bring 6+ years of experience scoping, building, and analyzing ML-powered systems, including models shipped to production.
Have significant experience with Python, scikit-learn, and PySpark.
Have familiarity with software engineering practices including testing, modularization, and version control.
Have technical training and professional experience applying modern causal inference and causal analysis techniques.
Have experience working with found data, guiding instrumentation to generate new data, and implementing data transformations for complex analyses and ML systems.
Have hands-on experience designing, monitoring, and analyzing experiments in consumer technology.
Have experience building ML or running experiments at a consumer technology company.
Have prior experience leveraging LLMs in advanced data processing and analysis workflows.
Have strong communication and project leadership skills to influence cross-functional teams.
Solve ambiguous problems in a structured, hypothesis-driven, data-supported way.

Investigate revenue-generating opportunities and use data to size, scope, and measure product changes in Databricks.
Design, build, deploy, and operate production ML systems for personalization, experimentation, and automation.
Use batch inference, online services, and online learning models with the team's feature store and model registry.
Partner with Product, Mobile Engineering, Cloud Engineering, Data Engineering, and MLOps to integrate ML systems into user-facing features.
Set up monitoring to measure ML feature performance and business impact.
Implement lineage tracking for data, code, and model artifacts.
Improve data pipelines that support experimentation and ML with Data Engineering.
Mentor data scientists and define best practices for advanced analytics and ML system development.
Use Claude Code and other AI tools for data discovery, modeling, and experiment evaluation.

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