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Salary: USD 175,000 - 218,000 / annual
Life360 is a consumer mobile app and location-tracking platform serving 102.4 million monthly active users across 180+ countries. The company offers location sharing, safe driver reports, crash detection with emergency dispatch, and pet GPS tracking.
The Revenue Experimentation team (Direct Revenue org) is the engine behind Life360's growth and monetization. This team designs, ships, and analyzes high-velocity experiments across ads, subscriptions, and one-time purchases—pricing, paywalls, ad placement and mediation, upsell and upgrade flows—ensuring every monetization decision is grounded in evidence. The team is expanding to cover consumer-facing ads, including recommendations and personalization.
As Lead Data Scientist, you will own the path from revenue opportunity to production ML for Direct Revenue. You will find opportunities, size them, agree on priorities with business leads, and ship models alongside the team's data scientists and engineers. In your first year, you will help the team scale the amount of production ML it delivers and help set the team's roadmap with the data science lead and the product manager.
Key responsibilities:
- Conduct deep investigations into revenue-generating options and use data to size, scope, and measure product changes in Databricks.
- Design, build, deploy, and operate production ML systems (batch inference, online services, online learning models) using the team's feature store and model registry for personalization, experimentation, and automation.
- Partner with Product, Mobile Engineering, Cloud Engineering, Data Engineering, and MLOps to integrate ML systems into user-facing features.
- Set up monitoring for new ML-based features to measure performance and business impact.
- Implement lineage tracking for data, code, and model artifacts to keep the ML lifecycle compliant, reproducible, and secure.
- Improve the data ecosystem with Data Engineering, including pipelines that feed experimentation and ML.
- Mentor other data scientists and define best practices for advanced analytics and ML system development.
- Use Claude Code and other AI tools across data discovery, modeling, and experiment evaluation.
Life360 is an AI-native company where AI is integral to how they build and operate. The team treats AI as a first-class collaborator at every stage: ideation, coding, testing, review, and iteration. They ship faster and go deeper because of it.
Life360 is remote-first with 500+ employees. The company offers comprehensive benefits including medical, dental, vision, life and disability insurance (100% paid for US employees), 401(k) with matching (US) or RRSP with DPSP (Canada), paid parental leave, mental wellness programs, flexible PTO, learning & development, equipment support, and free Life360 Platinum Membership and Tile trackers.
REQUIREMENTS:
- Education: Advanced degree in a field that relies on sophisticated statistical analysis or equivalent industry experience.
- Professional Experience: 6+ years of experience scoping, building, and analyzing ML-powered systems, including models shipped to production.
- Programming Proficiency: Significant experience with Python and scikit-learn, PySpark, and causal inference programming languages, including familiarity with software engineering best practices (testing, modularization, version control).
- Causal Inference: Technical training and professional experience applying modern causal inference and causal analysis techniques.
- Instrumentation and Measurement: Significant experience working with found data, guiding instrumentation to generate new data, and implementing data transformations to support complex analyses and ML system development.
- Experimentation: Hands-on experience setting up experiments in the consumer technology space, including experiment design, monitoring, and analysis of results.
- Consumer Product Experience: Experience building ML or running experiments at a consumer (B2C) technology company, ideally on revenue work such as subscriptions, transactions, or ads.
- Communication and Leadership: Strong communication and project leadership skills, with the ability to influence cross-functional teams.
- Problem Solving: Strong capability to solve ambiguous problems in a structured, hypothesis-driven, data-supported way.
- Generative AI & LLMs: Prior experience leveraging LLMs in advanced data processing and analysis workflows.
PREFERRED QUALIFICATIONS:
- Digital Advertising: Experience with modern digital advertising, including ad tech infrastructure.
- Feeds and Ranking: Experience with ranking and grouping algorithms for content feeds.
- Advanced Tooling: Experience contributing to and managing production feature stores and ML model registries.
- Revenue Partnership: Experience working with product line GMs, including familiarity with revenue reporting and forecasting analyses.
- Subscription Products: Experience in subscription-based products, lifecycle marketing, or user acquisition.
- Geospatial: Experience with geospatial data and mobile location-based services.
AI-NATIVE EXPECTATIONS:
- Daily use: Running parallel workstreams instead of hand-holding one agent at a time.
- Judgment and ownership: Review AI-generated code, analyses, and models as critically as a colleague's pull request. You are accountable for everything you ship, whoever or whatever wrote it.
- Velocity: Turn AI fluency into output. Deliver more production ML, faster, than the same work would take without AI.
- Team leadership: Share your AI workflows with the team and raise the AI fluency of the data scientists and engineers around you.
- Continuous learning: Keep up with new AI tools and techniques, test them on real team problems, and bring recommendations to the team.
