Director of Data Science, Life360

• 10+ years in Data Science, ML, or advanced analytics roles with 5+ years of team leadership
• Track record of deploying AI/ML systems in production to drive measurable business impact
• Mastery of machine learning (e.g., classification, clustering, time series, LTV/churn models), with hands-on experience using Python, scikit-learn, XGBoost, TensorFlow/PyTorch, etc
• Strong understanding of causal inference frameworks (e.g., RCTs, IVs, diff-in-diff, matching) and their application to B2C growth
• Deep experimentation expertise, including experience running A/B/n, multi-variate, and sequential tests at scale
• Business acumen and storytelling ability; able to drive executive buy-in with clarity and precision

Desirable

• We are seeking a Director of Data Science to lead the development and integration of AI, machine learning, and data science capabilities across Life360’s diverse product and business domains.
• This leader will partner with teams spanning core product, user experience, international and domestic growth, ads, marketing, finance, and user acquisition to identify high-impact opportunities where intelligent systems and scientific methods can enhance decision-making and unlock new value.
• The role will be instrumental in shaping a cross-functional roadmap that embeds AI/ML thinking into every stage of the product and business lifecycle, empowering teams to accelerate innovation, personalize experiences, and scale operational excellence
• You will be responsible for leading a world-class team of scientists focused on building next-generation machine learning systems, deploying AI to personalize user journeys, and high-impact experimentation and causal inference.
• This role is perfect for a visionary leader passionate about shaping the future of AI-enabled growth in consumer mobile products
• AI/ML-Driven Growth Innovation
• Lead the enterprise strategy for embedding AI and ML across the full customer lifecycle—from acquisition and onboarding to engagement, monetization, and retention—transforming how the business personalizes experiences, predicts behavior, and drives long-term value
• Define and scale intelligent decision systems that power dynamic, real-time interactions using advanced frameworks such as reinforcement learning, multi-armed bandits, and recommender systems. These systems serve as foundational infrastructure for continuously optimizing user experience, pricing, and content delivery across touchpoints
• Drive cross-functional alignment with engineering, product, and marketing to productionize ML capabilities that personalize every stage of the customer journey—from initial exposure through subscription experiences to ongoing engagement and reactivation—maximizing both user satisfaction and business impact
• Establish an enterprise-wide experimentation strategy that integrates AI-first methods, including causal inference, uplift modeling, inverse propensity scoring, and synthetic controls—unlocking more granular insights, accelerating learning cycles, and driving confident decision-making at scale
• Act as a thought leader in data-driven growth and customer intelligence, translating technical innovation into strategic advantage while scaling organizational maturity in machine learning adoption and responsible AI use
• Data Science & Experimentation Leadership
• Causal inference & uplift modeling: Go beyond average treatment effects to model heterogeneous effects and counterfactual outcomes
• Experimentation at scale: Develop adaptive experiment designs (e.g., multi-armed bandits, Bayesian optimization) to learn faster and allocate traffic dynamically
• Synthetic experiments: Use observational techniques (e.g., inverse propensity scoring, matching, synthetic controls) when randomized testing isn’t possible
• Simulation & modeling: Create simulation frameworks to test experimental sensitivity, estimate statistical power, and de-risk key decisions
• Experimentation infrastructure: Contribute to and architect platforms that support experimentation-as-a-service, including automated assignment, metric tracking, and runtime analysis
• Team & Thought Leadership
• Build and mentor a high-performing team of ML scientists, statisticians, and growth analysts
• Serve as a cross-functional thought partner to Product, Marketing, Engineering, and Lifecycle teams
• Foster a culture of innovation, experimentation, and continuous learning across the org