Product Data Analyst

This is a fully remote position, open to applicants in New York.

📋 Description

• You will take ownership of the metrics that drive the business.

• You will manage the dashboards and definitions that Product, Growth, Marketing, and Finance teams use daily.

• You will create in Hex, document in our specifications, and ensure consistent definitions across various teams.

• When there is a drop in retention or a leak in the funnel, you will look beyond the symptoms.

• You will trace the issue — through cohorts, segments, and the specific quirks of certain games — until you identify the root cause and propose a solution.

• You will design and evaluate A/B tests related to product modifications, pricing strategies, onboarding processes, and growth initiatives.

• You will determine sample sizes, assess statistical significance, and write the analyses.

• You will collaborate directly with our leaders on complex questions that lack straightforward answers.

• You will define the question, conduct the analysis, and present your recommendations.

⛳️ Requirements

• A minimum of 5 years of experience in a Data Analyst, Product Analyst, or Business Intelligence role (excluding internships).

• Proficiency in SQL — including complex joins, incremental computation, window functions, and query optimization; comfortable handling large, messy datasets.

• Experience with Python for analysis — including pandas and statistical libraries; you are adept at coding when necessary.

• Strong skills in experimentation — hypothesis testing, sample sizing, and the ability to distinguish a p-value from actionable insights.

• A proven track record of influencing metrics that you were passionate about (we will ask you to elaborate on one).

• Strong communication skills — you can convey difficult messages to leadership without obscuring the facts with caveats.

• A degree in a quantitative discipline.

• Familiarity with Hex, dbt, and BigQuery.

• Previous experience in growth-stage consumer software or gaming sectors.

• Knowledge of causal inference methods (such as difference-in-differences, regression discontinuity, and propensity score matching).

• Prior work involving LTV, retention, or subscription models.

• Experience in creating metrics frameworks or KPI hierarchies from the ground up.

🏝️ Benefits

• Competitive salary and performance-based bonuses.

• Comprehensive health and wellness benefits.

• Opportunities for professional development and career advancement.

• Flexible work hours and a supportive work environment.

• Access to cutting-edge tools and technologies.