Staff Decision Scientist, Advanced Analytics

Role overview

Set the analytical methods and standards used to understand active-member growth across a consumer product. This staff-level role works across teams rather than embedding in one operating group, leading experimentation and causal inference practices while owning the active-user forecast and growth model used in business planning. The emphasis is statistical reasoning, forecasting, and enabling other analysts—not production machine learning.

Responsibilities

- Build and maintain a forecast that decomposes monthly active users into registration, retention, and reactivation; document assumptions, track accuracy, and explain forecast gaps. - Establish standards for experiment design and analysis, including power, metric choice, variance reduction, sequential testing, and effects across connected users. - Apply suitable causal inference methods when randomized experiments are not practical, and turn useful analyses into reusable approaches. - Review work across teams, document analytical practices, and advise on difficult methodological questions. - Partner with decision scientists, data scientists, and data engineering teams to make rigorous methods usable and repeatable. - Use AI tools to help scale analytical workflows and improve consistency across the team.

Requirements

- Eight or more years in analytics, data science, or decision science at a consumer technology company. - Advanced quantitative training or equivalent practical experience. - Applied expertise in causal inference and large-scale experiment design, including network effects or interference. - Experience owning forecasts or growth models for a core business metric, using cohort- or component-based methods. - Experience with experimentation platforms and proficiency in SQL plus Python or R. - Record of setting analytical standards, mentoring colleagues, and influencing teams without direct authority.

Nice to have

- Experience with subscription or freemium products, growth, retention, or lifecycle analytics. - Bayesian methods, probabilistic programming, lifetime-value modeling, incrementality testing, or marketing mix modeling. - Experience with analytics tooling, AI agent workflows, dbt, or international and multi-market analysis.

Benefits and work setup

- Remote-first work within the United States or Canada. - Benefits include health coverage, retirement savings programs, paid parental leave, flexible paid time off, company holidays and seasonal shutdowns, wellness support, learning programs, and equipment support.