Staff Data Scientist - Ads (AI Native)

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 97.8 million monthly active users (MAU), as of March 31, 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 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 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.

About the Job As a Staff Data Scientist on the Ads team, you'll own deep analysis of technical problems in ads delivery and translate that analysis into algorithmic solutions — then work directly with engineers to implement, deploy, and operate what you build.

This role sits inside the Ads business unit, one of Life360 's core revenue lines, and you'll partner closely with other Data Scientists and engineers to turn data and models into systems that increase the scale and efficacy of the ads served across our infrastructure.

We're hiring for this role now because the team is scaling ads-optimization models beyond initial pilots and needs a senior specialist to own that transition end to end.

In your first year, success looks like taking at least one ads-optimization model from prototype to production and measurably improving a delivery metric — such as bid efficiency or inference latency — at scale. For candidates based in the US, the salary range for this position is $137,000 to $252,000 USD.

For candidates based out of Canada, the salary range for this position is $198,000 to $233,000 CAD. We take into consideration an individual's background and experience in determining final salary; therefore, base pay offered may vary considerably depending on geographic location, job-related knowledge, skills, and experience.

The compensation package includes a wide range of medical, dental, vision, financial, and other benefits, as well as equity. 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.

Professional Experience: 8+ years of experience analyzing, implementing, and operating machine learning and/or optimization systems.

Programming Mastery: Strong proficiency in Python with deep familiarity with software engineering best practices (testing, modularization, version control, etc.).

MLOps and Datastore Tooling: Familiarity with specialized ML lifecycle and data processing tools and platforms such as MLflow, Kubeflow, SparkML, Synapse ML, SQL, Spark/PySpark, dbt, and Airflow.

Cloud Foundations: Practical experience operating within a major cloud ecosystem—e.g., AWS, GCP, Databricks—with a clear grasp of cloud networking, security, and storage tiers.

Strong communication and project leadership skills, with the ability to influence cross-functional teams.

Nice to Have Hands-on experience formulating and solving optimization problems (e.g., linear programming, mixed-integer programming) for advertising use cases such as budget allocation, bid optimization, or audience targeting. Core Expectations

Problem-solving mindset - You structure ambiguous problems precisely before reaching for a tool, AI or otherwise

Collaborative approach - You can explain technical tradeoffs and articulate ideas effectively, work well across teams, and value diverse perspectives

Ownership mentality - You take responsibility for your work from design through production and beyond

AI-Native Expectations

Daily use - You use AI tooling (Claude Code or equivalent) as a genuine development partner every day: delegating discrete implementation tasks, running parallel workstreams, and writing the prompts and specs that make that possible

Judgment and ownership - You review AI-generated code, analysis, and models critically before they ship, and you're accountable for what reaches production regardless of whether a human or an agent wrote it