[All remote jobs](https://jobicy.com/jobs.md)[![Life360 logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2022/02/f269bad3-221.jpeg)](https://jobicy.com/company/tile.md)[Life360](https://jobicy.com/company/tile.md) # Lead Data Scientist Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/tile.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md), [Canada](https://jobicy.com/job-region/canada.md)SalaryUSD 175k–218k / yrDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceSeniorPublished20 Oct 2026Apply before19 Nov 2026Listing views49Application actions4Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Life360 is hiring a Lead Data Scientist to lead revenue experimentation and production machine-learning initiatives across advertising, subscriptions, purchases, and personalization. The role combines opportunity sizing, causal analysis, experiment design, ML engineering, and ongoing performance monitoring for a large consumer product. The successful candidate will partner closely with product, mobile, cloud, data engineering, and MLOps teams while mentoring other scientists and shaping the ML roadmap. Strong hands-on expertise in Python, PySpark, scikit-learn, causal inference, experimentation, and production ML systems is central to the position. The role also expects high ownership and effective use of AI tools to increase delivery velocity without compromising review standards. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThis is a senior, high-impact role requiring ownership from ambiguous revenue opportunities through reliable production ML delivery. It demands deep statistical rigor, engineering maturity, cross-functional influence, and leadership across a high-velocity experimentation environment. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$196,500US market range (per year)$170k–$240k0$264k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. The stated US base salary range is $175,000–$218,000 USD; its midpoint is $196,500 annually. The posting does not explicitly state a cadence, so yearly pay is inferred from the full-time senior US role and the magnitude of the disclosed salary range. A reasonable estimated US annual market range for a lead-level data scientist with production ML and experimentation leadership responsibilities is $170,000–$240,000 USD. Canadian candidates are separately offered $171,500–$202,500 CAD annually inferred on the same basis. ## Core skills Skills and capabilities most closely associated with this opportunity. [Python](https://jobicy.com/jobs?search_keywords=Python.md)[PySpark](https://jobicy.com/jobs?search_keywords=PySpark.md)[scikit-learn](https://jobicy.com/jobs?search_keywords=scikit-learn.md)[Causal inference](https://jobicy.com/jobs?search_keywords=Causal%20inference.md)[A/B testing](https://jobicy.com/jobs?search_keywords=AB%20testing.md)[Machine learning operations](https://jobicy.com/jobs?search_keywords=Machine%20learning%20operations.md)[Feature stores](https://jobicy.com/jobs?search_keywords=Feature%20stores.md)[Model registries](https://jobicy.com/jobs?search_keywords=Model%20registries.md)[Databricks](https://jobicy.com/jobs?search_keywords=Databricks.md)[Revenue analytics](https://jobicy.com/jobs?search_keywords=Revenue%20analytics.md)[Personalization](https://jobicy.com/jobs?search_keywords=Personalization.md)[Data pipeline development](https://jobicy.com/jobs?search_keywords=Data%20pipeline%20development.md)[ML monitoring](https://jobicy.com/jobs?search_keywords=ML%20monitoring.md)[SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[Cross-functional leadership](https://jobicy.com/jobs?search_keywords=Cross-functional%20leadership.md) Sample interview questionsDescribe a production ML system you owned from initial opportunity assessment through deployment and monitoring.I would explain how I quantified the business opportunity, defined success metrics and constraints, built a reproducible training pipeline, partnered with engineering on deployment, and monitored both model quality and business impact after launch. I would also describe rollback criteria and the iteration process. How would you evaluate whether a new paywall design causes an increase in subscription revenue rather than simply shifting user behavior? I would define the causal estimand, randomize eligible users into treatment and control groups, validate assignment and instrumentation, and predefine primary and guardrail metrics. I would analyze incremental conversion and revenue with confidence intervals, check heterogeneous effects, and account for downstream retention or cannibalization effects. What practices do you use to make ML systems reproducible and compliant? I use version control for code, tracked data and feature definitions, experiment logging, model registry versioning, automated tests, documented lineage, and access controls. I also ensure that training-serving consistency, evaluation artifacts, and deployment approvals are auditable. How would you prioritize competing ML opportunities across advertising, subscriptions, and one-time purchases? I would assess expected incremental value, confidence in the estimate, time to learn, implementation cost, risk, and strategic alignment. I would compare opportunities through a transparent prioritization framework, validate assumptions with targeted experiments, and align the roadmap with product and business leaders. How do you use AI coding or analysis tools while maintaining technical accountability? I use AI tools to accelerate exploration, boilerplate development, test creation, and analysis workflows, but I independently validate logic, data assumptions, security, and statistical conclusions. I treat generated work like a colleague’s pull request: it requires review, testing, and clear ownership before it reaches production. Opportunity details ## About this role. ### 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](http://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 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. ### Our Benefits * Medical, dental, vision, life and disability insurance plans (100% paid for US employees). We offer life and disability as well as supplemental plans for medical and dental for Canadian employees. * 401(k) plan with company matching program in the US and RRSP with DPSP plan for Canadian employees. * Paid parental leave. * Mental Wellness Program & Employee Assistance Program (EAP) for mental well-being. * Generous time off: flexible PTO, companywide holidays throughout the year plus summer and winter shutdowns. * Learning & Development programs. * Equipment, tools, and reimbursement support for a productive remote environment. * Free Life360 Platinum Membership and Tile trackers. ### Life360 Values Our company’s mission-driven culture is guided by our shared values to create a trusted work environment where you can bring your authentic self to work and make a positive difference * Be a Good Person – We have a team of high integrity people you can trust. * Be Direct With Respect – We communicate directly, even when it’s hard. * Members Before Metrics – We focus on building an exceptional experience for families. * High Intensity, High Impact – We do whatever it takes to get the job done. ### Our Commitment to Diversity We believe that different ideas, perspectives and backgrounds create a stronger and more creative work environment that delivers better results. Together, we continue to build an inclusive culture that encourages, supports, and celebrates the diverse voices of our employees. It fuels our innovation and connects us closer to our customers and the communities we serve. We strive to create a workplace that reflects the communities we serve and where everyone feels empowered to bring their authentic best selves to work. We are an equal opportunity employer and value diversity at Life360. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status or any legally protected status. We encourage people of all backgrounds to apply. We believe that a diversity of perspectives and experiences create a foundation for the best ideas. Come join us in building something meaningful. 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