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Salary: USD 279,000 - 310,000 / annual
Discord is seeking a Staff Data Scientist to lead the vision and strategy of its Experimentation Platform, which runs hundreds of experiments at any given time to drive business decisions across the company. This is a high-impact role on a small, rapidly growing team with significant leadership opportunities.
You will be responsible for ensuring the statistical rigor of Discord's experimentation methodologies, partnering with engineering, product, and data science teams to improve the reliability and scalability of the platform. Key responsibilities include formulating the platform's roadmap, providing statistical expertise on causal inference and experimental design, and leading cross-functional initiatives to educate teams on experimentation best practices.
You will directly empower Discord's 50+ member Data Science team to adopt more rigorous causal inference methods, conduct original research on high-priority questions, and engage with experimentation customers (data scientists, product managers, engineers) to ensure the platform enables fast, reliable, data-driven decisions. The role includes designing and delivering training programs, workshops, and educational materials on experimentation design and statistical methodology.
Required qualifications include a PhD in a quantitative field (Statistics, Economics, Political Science, Psychology) or equivalent practical experience, plus 4+ years designing, implementing, and analyzing experiments or causal inference projects. You should have proven experience leading experimentation platform work, strong ability to evaluate and recommend statistical approaches that balance velocity and reliability, and demonstrated passion for education and fostering data literacy. Proficiency in Python, SQL, R, or other statistical programming languages is essential.
Bonus qualifications include conducting literature reviews and championing scientific best practices, track record using causal inference methods that translated to business outcomes, autonomous leadership of cross-functional projects, comfort with AI/LLM tools to accelerate workflows, and causal inference-related publications.
