Lead Data Scientist

About Life360

Life360’s mission is to keep people close to the ones they love. Our category-leading mobile app, Tile tracking devices, and Pet GPS tracker empower members to protect the people, pets, and things they care about most with a range of services, including location sharing, safe driver reports, and crash detection with emergency dispatch. Life360 serves approximately 102.4 million monthly active users (MAU), as of June 30, 2026, across more than 180 countries.

Life360 delivers peace of mind and enhances everyday family life with seamless coordination for all the moments that matter, big and small. By continuing to innovate and deliver for our customers, we have become a household name and the must-have mobile-based membership for families (and those friends who are basically family).

Life360 has more than 500 (and growing!) remote-first employees. For more information, please visit life360.com.

Life360 is a Remote First company, which means a remote work environment will be the primary experience for all employees. All positions, unless otherwise specified, can be performed remotely (within the US and Canada) regardless of any specified location above.

We are AI Native

We are building an AI native company where AI is an integral part of how we build and operate. AI tool usage during interviews varies by role. You may be asked to demonstrate proficiency with AI tools, discuss how you leverage AI, or complete interview exercises without AI assistance. Your Recruiter will provide clear guidance as you move through the interview process.

Undisclosed use of AI not previously discussed with or approved by your Recruiter may impact your candidacy.

About The Team

The Revenue Experimentation team (Direct Revenue org) is the engine behind how we grow and monetize Life360 responsibly. We design, ship, and analyze high-velocity experiments across ads, subscriptions, and one-time purchases — pricing, paywalls, ad placement and mediation, upsell and upgrade flows — so every monetization decision is grounded in evidence, not instinct. Our work directly shapes the revenue behind nearly 100 million members. We are a product-minded, AI-Native engineering team. AI isn't just a tool we use — it's how we work. We treat it as a first-class collaborator at every stage: ideation, coding, testing, review, and iteration. We ship faster and go deeper because of it, and we're looking for someone who wants to help define what that looks like at scale.

About the Job

As a Lead Data Scientist, you will own the path from revenue opportunity to production ML for Direct Revenue, the business unit responsible for growing active users, subscriptions, revenue-generating transactions, and in-app advertising across Life360's more than 100 million monthly active users. You will find the opportunities, size them, agree on priorities with business leads, and ship the models alongside the team's data scientists and engineers.

This role matters now because the team is expanding to cover consumer-facing ads, including recommendations and personalization, and needs a senior scientist who pairs a deep understanding of the problem with the ability to put models into production. 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.

For candidates based in the US, the salary range for this position is $175,000 to $218,000 USD. For candidates based out of Canada, the salary range for this position is $171,500 to $202,500 CAD. We take into consideration an individual's background, job-related knowledge, skills, and experience in determining final salary. Base pay may also vary based on geographic location, with US work locations falling into one of three geographic tiers.

Total compensation is inclusive of equity compensation in the form of Restricted Stock Units (RSUs), as well as a comprehensive benefits package, including medical, dental, vision, financial, and other benefits.

What You’ll Do

- Conduct deep investigations into revenue-generating options and the data behind them, and use them to size, scope, and measure product changes in Databricks.

- Design, build, deploy, and operate production ML systems, from batch inference to online services to online learning models, using the team's feature store and model registry, for personalization, experimentation, and automation use cases.

- Partner with Product, Mobile Engineering, Cloud Engineering, Data Engineering, and MLOps to integrate these systems into user-facing features.

- Set up monitoring for new ML-based features so their performance and business impact are measured.

- 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 the 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.

What We’re Looking For

Expected Experience & Qualifications

- 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 you have 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, etc.).

- 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. We expect you to 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