Staff Decision Scientist, Advanced Analytics

Description

The company makes a family safety app along with tracking devices and a pet GPS tracker. Its features include location sharing, safe driver reports, and crash detection with emergency dispatch. As of June 30, 2026, it had roughly 102.4 million monthly active users (MAU) across more than 180 countries.

The company is remote-first, with 500+ employees.

The Data & Analytics (DnA) group has 60+ people across data engineering, analytics engineering, analytics and decision science, and data science and ML. The science work splits into 2 sub teams, one for MAU and one for Revenue. This role leads decision science and data science for MAU.

The team works out why members join, stay, and come back, then partners with product, marketing, and engineering to act on those findings. The findings shape the roadmaps.

The data is unusual. Measurement covers families and circles rather than individuals, so one person's behavior affects others. Billions of location events, driving behavior, crash detection, and tracker data give a detailed view of physical activity.

This Staff Decision Scientist role leads Advanced Analytics, which owns the methods, models, and standards the rest of the team uses. The role is not tied to one operating group (OG). It covers all of them, improves how experiments and causal inference are run, and owns the MAU forecast and growth model that leadership uses for planning. It reports to DnA leadership.

Most of the impact comes through other people. You work with decision scientists, data scientists, analytics engineers, and data engineers, and use AI to make advanced methods repeatable. This is not a production ML role, since a separate DS/MLE team covers that. The core tools are statistical inference, forecasting, and judging which method fits a question.

In year one, success means the team has adopted a standard for experimentation and causal inference, and leadership uses an MAU forecast whose accuracy is tracked.

Responsibilities

• Own the MAU forecast and growth model, built in Python and SQL. It splits MAU into registration, retention, and reactivation, and leadership uses it for planning and setting targets. State the assumptions, track accuracy, and explain misses.
• Set the experiment standard: power, metric choice, variance reduction, sequential testing, heterogeneous effects, and interference when users share circles with family and friends. Be the reviewer the team goes to for high-stakes tests.
• Advance causal inference. When a clean experiment is not possible, apply quasi-experimental methods such as difference-in-differences, synthetic controls, regression discontinuity, and instrumental variables. Turn one-off analyses into approaches others can reuse.
• Define and document best practices for methods. Review analyses across OGs and answer the hardest methods questions.
• Work with OG decision scientists on their toughest problems, with data scientists on modeling choices, and with analytics and data engineers to build durable data models, metrics, and tooling.
• Build AI agent workflows (Claude Code or similar), templates, and tools so the team can run forecasts, experiment readouts, and causal analyses faster and more consistently. Automate any analysis that gets repeated.
• Mentor through coaching, analysis and code reviews, internal talks, and written guides. Raise the level of senior ICs and the baseline for everyone else.
• Turn forecasts and causal findings into clear recommendations with a point of view. A strong result that nobody acts on has not succeeded.

Requirements

• Know the assumptions behind each method, when they fail, and when a simpler approach works better.
• Own the forecast and the standards end to end, including checking that they still hold and fixing them when they don't.
• Use AI tools such as Claude Code or similar as a working partner: hand off discrete tasks, review outputs critically, and run parallel workstreams. Think about how AI can scale the whole team's work.
• Measure success by how much the team's work improves. Enjoy teaching and be patient with people learning new methods.
• Drive adoption of standards across teams you don't manage by making a clear case and making the right approach the easy one.
• 8+ years in analytics, data science, or decision science at a consumer tech company.
• An advanced degree in a quantitative field such as economics, statistics, quantitative social science, or operations research, or equivalent practical experience.
• Deep applied experience with causal inference methods such as difference-in-differences, instrumental variables, regression discontinuity, synthetic controls, and propensity score matching.
• Experience building and owning a forecast or growth model for a core metric such as MAU, DAU, subscribers, or revenue, using cohort or component approaches.
• Expertise in experiment design and analysis at scale, including variance reduction, sequential testing, and network effects or interference.
• Experience with experimentation platforms such as Statsig or Optimizely.
• A track record of setting analytical standards and mentoring data scientists.
• Proficiency in SQL and in Python or R for statistical analysis.
• Preferred: experience with subscription or freemium business models.
• Preferred: background in growth, retention, or lifecycle analytics.
• Preferred: experience with Bayesian methods or probabilistic programming, such as PyMC or Stan.
• Preferred: experience with LTV modeling, incrementality testing, or marketing mix modeling.
• Preferred: experience building internal tooling or AI agent workflows for analytics teams.
• Preferred: experience building dbt models or contributing to analytics engineering workflows.
• Preferred: familiarity with international or multi-market analytics.

Conditions

The company is remote-first, and the main work setup is remote. The role can be done remotely within the US and Canada, regardless of the location listed above.

US-based pay range: $130,500 to $242,500 USD. Canada-based pay range: 180,500 to 214,000 CAD. Final pay depends on background, job knowledge, skills, and experience. Base pay may also vary by location, and US work locations fall into 3 geographic tiers.

Total compensation includes Restricted Stock Units (RSUs) along with the benefits package.

AI use in interviews depends on the role. You may be asked to show AI skills, talk through how you use AI, or complete an exercise without AI. Your recruiter will explain the process. Using AI without telling your recruiter or getting approval first may affect your candidacy.

The company uses AI to make hiring more efficient, but hiring teams make every hiring decision.

The company is an equal opportunity employer and encourages you to apply even if you don't meet every qualification.

Benefits

• Medical, dental, vision, life, and disability insurance, 100% paid for US employees.
• Canadian employees receive life and disability coverage plus supplemental medical and dental plans.
• A 401(k) with company matching in the US, and an RRSP with a DPSP for Canadian employees.
• Paid parental leave.
• A mental wellness program and an Employee Assistance Program (EAP).
• Flexible PTO, companywide holidays throughout the year, and summer and winter shutdowns.
• Learning and development programs.
• Equipment, tools, and reimbursement to support a productive remote setup.
• A free top-tier membership to the company's family app, plus free tracking devices.