Join the VRChat Team!

VRChat offers a first-of-its-kind, game-changing platform that provides an endless collection of social VR experiences and gives the power of creation to its robust community. With over 250,000 worlds and growing, VRChat’s vision is to allow users to bring their imaginations to life and help shape the metaverse anywhere in the world on any device. VRChat has raised around $100M to date with the support of our investors, which include: Makers Fund, Anthos Capital and HTC. We have a great team which includes people from: Netflix, Twitter, Meta, Microsoft, Roblox, Google, Amazon, Unity, Spotify, Discord, Uber, eBay, Robinhood, Twitch, Zynga and TikTok.

Come and join the mission!

Job Overview

As a Senior Data Scientist on VRChat’s Data Team, you will play a key role in driving strategic product decisions and advancing our product roadmap across growth, user experience, creator, and economy verticals. The ideal candidate will have a background in a technical field, will have experience working with large production data sets, and will have some experience delivering data products on a fast-paced team.

This position will be full-time and remote.

What You’ll Do

Partner with cross-functional teams to define high-impact product questions, success metrics, and decision frameworks.

Analyze user behavior, creator activity, content ecosystems, retention, growth, monetization, and social dynamics to identify opportunities and risks.

Design and analyze experiments, including A/B tests and other causal measurement approaches, to evaluate product launches, campaigns, and strategic initiatives.

Define and improve core product metrics, dashboards, diagnostic views, and recurring reporting that help teams understand what is changing and why.

Build durable analytical assets such as reusable notebooks, data marts, metric definitions, self-serve dashboards, and analysis templates.

Use AI-assisted and agentic workflows to accelerate analysis, automate repetitive investigation, improve documentation, and build lightweight internal tools for common analytical questions.

Collaborate with Data Engineering and product teams to improve data quality, instrumentation, modeling, and source-of-truth definitions.

Communicate findings clearly to technical and non-technical audiences, including senior stakeholders, and influence product decisions through evidence and judgment.

What We’re Looking For

5+ years of experience in data science, product analytics, data analytics, quantitative analysis, or a related analytical role.

Expert SQL skills and strong proficiency with Python or R for analysis, experimentation, modeling, automation, and reproducible workflows.

Strong product sense, with experience translating ambiguous product or business problems into structured analyses, metrics, experiments, and recommendations.

Experience designing, analyzing, and interpreting experiments, including A/B tests, guardrail metrics, and common statistical pitfalls.

Experience building dashboards, reporting systems, reusable analyses, or data products that help stakeholders self-serve and make better decisions.

Familiarity with AI-assisted analytical workflows, such as using LLMs or agents for code generation, exploratory analysis, documentation, QA, summarization, or workflow automation.

Strong judgment around data quality, statistical validity, reproducibility, privacy, and the limitations of AI-generated analysis.

Excellent communication and stakeholder management skills, including the ability to explain complex findings clearly and influence senior audiences.

Bachelor’s or Master’s degree in a technical, quantitative, or data-related field, or equivalent practical experience.

Bonus Points

Experience at a social, gaming, creator economy, marketplace, consumer subscription, or UGC platform company.

Experience with social graphs, creator ecosystems, virtual economies, recommendations, lifecycle analytics, retention, churn, pricing, trust & safety, or community health metrics.

Experience building AI-powered internal tools, analytical agents, natural-language data interfaces, automated reporting, or self-service insight systems.

Experience with tools such as dbt, Snowflake, BigQuery, Tableau, Amplitude, Hex, Mode, Jupyter, Streamlit, Airflow, Dagster, or similar platforms.

Experience mentoring other data scientists, analysts, or cross-functional partners.