Lead Decision Scientist, Product

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.

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.

We're hiring a Lead Decision Scientist to be a strategic partner embedded with product and engineering teams. Someone who shapes what gets built, how we grow, and where we invest. You'll own the analytical narrative for your area and deliver insights that directly change roadmaps and resource allocation.

The output is decisions, not dashboards. You'll also help make the AI-native organization a reality, shaping the tools and workflows that let automation take on more so the team can go deeper. 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, data scientists, and ML engineers.

For candidates based in the US, the salary range for this position is $133,000 to $195,000 USD. For candidates based out of Canada, the salary range for this position is 147,500 to 173,000 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.

• Be the strategic thought partner for cross-functional teams (PMs, engineers, marketing, finance).You understand the roadmaps and users, and you notice the gap between what the data shows and what the team assumes.

• Tell stories that move teams to act. You'll present to leadership and working teams with clear narratives and a point of view. A great insight that nobody acts on is a failed insight.

• Establish causality with the right tools for the situation, including A/B testing, analytics, and causal inference. You work with engineering to implement event instrumentation and with Product to translate insights into action.

• Work backward from an understanding of how users experience our product to develop and implement metrics strategies that measure what matters to our users and our business. This enables a deep understanding of our users and their motivation, in a complex ecosystem with multi-user engagement and many ways to interact with our product.

• Build explanations on top of measurement, always grounding analysis in the reality that users are people with motivations and context the data alone won't tell you.

• Use AI to multiply your impact. You'll use coding agents and automated analysis daily, and help shape what our AI-native analytics stack looks like, contributing to how we move from reactive to proactive to autonomous.

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

• Ownership mentality: You take responsibility for your work from framing the question through delivering the recommendation and tracking its impact.

• 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.

• Curiosity and initiative: You don't wait for the roadmap to tell you what to analyze. You dig into data because you're genuinely curious about how things work.

• 6+ years in an analytics, data science, or decision science role at a consumer tech company

• 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)

• Track record of influencing product or business strategy through data, with specific examples of cross-functional impact

• Experience with experimentation platforms (Statsig, Optimizely, or similar)

• Experience building dbt models or contributing to analytics engineering workflows

• Experience with LTV modeling, incrementality testing, or marketing mix modeling

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