Staff Decision Scientist, Direct Revenue (AI Native)

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 107.7 million monthly active users (MAU), as of September 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.

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.

No other team at Life360 touches every product decision, every experiment, and every revenue lever. Data & Analytics is a 60+ person organization spanning data engineering, analytics engineering, data science, and ML. We're building it to be AI-native, where automation handles the volume so our people can focus on understanding why users behave the way they do, establishing what actually causes what, and turning those findings into action.

Tens of millions of families use Life360 every month. Turning that trust into a durable subscription business, without breaking what makes members love the product, is one of the most consequential problems at the company. This role sits at the center of it.

As the Staff Decision Scientist for Direct Revenue, you'll be the analytical partner to the leaders who run our subscription business. You'll work on the questions that set the direction of the company: how we package and price membership, which new offerings are worth building, where and how we expand internationally, and what drives a member to subscribe, stay, or leave. Your analysis will go straight to the people making those calls, and you'll be in the room when they're made.

That means more than answering the questions you're given. You'll reframe the ones that are off, push back when the evidence points somewhere leadership doesn't want to go, and set honest expectations about what the data can and can't tell us. You'll also be a senior voice on the team, mentoring Decision Scientists and raising the bar for how we tell stories with data across the org.

This is not a production ML role (we have a dedicated Data Science / MLE team for that). Your primary tools are statistical inference, clear thinking, and the judgment to know which question matters most.

You'll report into the Data & Analytics leadership team and work alongside data engineers, analytics engineers, data scientists, and ML engineers.

For candidates based in the US, the salary range for this position is $118,500 to $218,500 USD. For candidates based out of Canada, the salary range for this position is 164,000 to 193,500 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. 

• →Be leadership's thought partner on monetization. Work directly with the executives who own subscription revenue. Bring them a point of view on where the next dollar of revenue comes from, and the evidence to back it.
• →Shape how we package, price, and sell membership. Lead analysis on tiers, pricing, paywalls, trials, and upgrade and downgrade paths, balancing near-term revenue against long-term member value.
• →Size and test new revenue opportunities. Build the case for (or against) new offerings and bundles across the app (Tile, Pet GPS, etc.) from first sizing through test design and post-launch read.
• →Guide international expansion. Help decide which markets to prioritize and how to adapt pricing and packaging by market, using local willingness-to-pay, conversion, and retention data.
• →Tell stories that change decisions. Turn complex analysis into a clear narrative with a recommendation. A great insight that nobody acts on is a failed insight.
• →Go beyond predictive correlation to causation. Use A/B tests, pricing experiments, and causal inference to measure what actually moves conversion, retention, and LTV.
• →Shape the AI-native analytics stack. Lead adoption of tools and agent workflows that make monetization analysis faster and more self-serve, so the team spends its time on judgment, not plumbing.
• →Push back and manage expectations. Say no to the wrong question, reset unrealistic targets with data, and keep stakeholders aligned on what we know, what we don't, and what it'll take to find out.
• →Mentor and multiply. Coach Decision Scientists on framing, causal rigor, and executive communication. Review their work, help them prepare for leadership readouts, and grow the next generation of strategic analysts.

• Executive presence: You're credible with senior leaders. You can hold a room, handle hard questions in real time, and disagree without damaging the relationship.
• Storytelling and clarity: You lead with the answer, cut what doesn't matter, and adapt the message to the audience, whether that's a CEO, a PM, or an engineer.
• Courage to push back: You'll tell leadership what the data says even when it's not what they hoped. You do it directly and with respect.
• Stakeholder judgment: You manage competing asks, set realistic expectations, and protect focus for the work that matters most.
• Ownership mentality: You take responsibility from framing the question through delivering the recommendation and tracking its impact. You're accountable for decisions changed, not analyses delivered.
• Commitment to mentorship: You get real satisfaction from making other people better, and you have a track record of doing it.
• AI-native working style: You use AI tooling (Claude Code or equivalent) as a genuine development partner: delegating discrete tasks, reviewing outputs critically, and running parallel workstreams.

• 8+ years in an analytics, data science, or decision science role at a consumer tech company.
• Direct experience with subscription monetization: pricing, packaging, paywalls, conversion, or churn.
• Advanced degree in a quantitative field (economics, statistics, quantitative social science, operations research) or equivalent practical experience.
• Demonstrated experience with causal inference methods in applied settings (e.g., difference-in-differences, instrumental variables, regression discontinuity, synthetic controls, propensity score matching).
• A track record of partnering with executives and influencing major revenue or strategy decisions, with specific examples.
• Experience designing and analyzing pricing or monetization experiments, using platforms such as Statsig, Optimizely, or similar.
• Experience formally or informally mentoring other analysts or data scientists.
• Proficiency in SQL and Python/R for statistical analysis.

• Experience with freemium or multi-tier subscription models.
• Experience with international pricing or multi-market launches.
• Familiarity with pricing and marketplace analytics.
• Experience building dbt models or contributing to analytics engineering workflows.
• Experience with hardware-plus-subscription bundles.

We expect everyone on Data & Analytics to work with AI every day. For this role, that looks like:

• Daily use: You use Claude Code or an equivalent tool as a working partner for SQL, Python/R analysis, experiment readouts, and first drafts of leadership memos. You delegate well-scoped tasks, run parallel workstreams, and can describe which parts of your last analysis AI handled and which you did yourself.
• Judgment and ownership: You review AI output critically before it reaches anyone. You check the queries, the statistical assumptions, and the causal claims. You are accountable for every number and recommendation you deliver, whoever or whatever drafted it.
• Velocity: You use AI to cover more ground. Sizing an opportunity, building a test design, or reading out a pricing experiment should take days, not weeks. You spend the time you save on framing the question and on the decision.
• Team leadership: You share what works with the Decision Scientists you mentor and with the wider team, such as prompts, agent workflows, and review checklists. You help raise the team’s AI fluency, not only your own.
• Continuous learning: You keep up with new tools and techniques for experimentation, causal inference, and monetization analytics, and you bring concrete recommendations to the team about what to adopt and what to skip.