Staff Data Scientist - Ads AI Native - Life360

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

Data Science and Machine Learning (DSML) at Life360 is a lean, high-impact, matrixed team of specialists embedded directly in business units, working cross-functionally with Product, Analytics, Engineering, and business stakeholders. The team supports a platform with roughly 99M viewable impressions daily, and builds the production, ML, and optimization systems behind subscriptions, partnerships, and ads revenue. We build with agentic AI by default — not because it's novel, but because it lets a small team ship and operate production ML at a pace a much larger team would otherwise need. We use tools like Claude Code to delegate implementation work so DSML's specialists can focus on the modeling and judgment calls AI can't make.

What You’ll Do

• Partner with Product, Data Science, Cloud Engineering, and Data Engineering to design, develop, and deploy machine learning and optimization solutions.
• Train, deploy, and scale machine learning models as high-availability microservices or batch processing workflows, working with infrastructure and backend engineers to integrate model outputs directly into our ads systems.
• Establish unified logging, alerting, and monitoring solutions to track model inference performance, system latency, resource utilization, data drift, and concept drift.
• Implement robust lineage tracking for data, code, and algorithmic artifacts to ensure compliance, reproducibility, and security across the entire development and operational lifecycle.
• Work with data engineering to improve the data ecosystem, ensuring robust, scalable pipelines for experimentation and ML.
• Mentor other Data Scientists and help define best practices and technical architectures for machine learning engineering and scalable ML service ops.
• Use agentic AI tools (Claude Code or equivalent) as a core part of your daily workflow — delegating implementation tasks, running parallel workstreams, and critically reviewing AI-generated code and analysis before it ships.
• Handle on-call rotation and address live production incidents.

What We’re Looking For

Required

• Education: Advanced degree in a quantitative field—or equivalent industry experience.