Discord's Experimentation Platform puts data at the heart of the company's decision-making and growth - we run hundreds of experiments at any given time, and those results turn directly into business decisions across Discord. 

As a Staff Data Scientist on this team, you will ensure that the statistical underpinnings of our experiments are sound, that experimenters are able to design experiments with high rigor, and that Discord makes the best business decisions on the basis of these experiments. We are a small and quickly growing team; you will be presented with significant leadership opportunities as we evolve.

Our team directly impacts the strategy and roadmaps that improve Discord for its more than 90M daily active users! If helping make Discord an even better place to hang out with your friends sounds like an exciting challenge - we'd love to chat with you!

What you will be doing

• Formulate the vision and set the roadmap for the future of the Experimentation Platform, in partnership with engineering, product, and data science stakeholders.
• Provide statistical expertise, ensuring that experimentation methodologies and frameworks are sound and aligned with best practices in causal inference and experimental design.
• Partner closely with engineering and product to improve the reliability, scalability, and adoption of experimentation across Discord - including modern AI/LLM tooling where it can accelerate rigor and speed.
• Lead initiatives to educate and train cross-functional teams - including workshops, training sessions, and educational materials - on experimentation design, statistical methodology, and causal inference.
• Empower Discord's Data Science team (50+ members) to adopt more rigorous causal inference methods.
• Lead and conduct original causal inference research on high-priority Discord questions, in partnership with the wider Data Science team.
• Engage directly with experimentation customers - data scientists, product managers, and engineers - to ensure the platform enables fast, reliable, data-driven decisions.

What you should have

• Proven experience leading experimentation platform work, including designing and validating statistical methodologies to ensure the accuracy and reliability of experimental results. 
• PhD in a quantitative field (e.g. Statistics, Economics, Political Science, Psychology) or equivalent practical experience, plus 4+ years designing, implementing, and analyzing experiments or causal inference projects. 
• Ability to critically evaluate and recommend statistical approaches that balance velocity and reliability across a variety of product launches.
• Strong passion for education, with a track record of communicating complex statistical and experimental design concepts to technical and non-technical audiences and fostering a culture of data literacy.
• Demonstrated experience in developing and delivering training programs or educational content related to experimentation, causal inference, or statistical analysis, with a track record of fostering a culture of data literacy within an organization.
• Proficient with Python, SQL, R, and/or other statistical programming languages.

Bonus Points

• Interest in conducting literature reviews, translating and championing best scientific practices across the company.
• Track record using causal inference methods that translated into business decisions and outcomes.
• Experience autonomously leading cross-functional projects and influencing cross-functional partners.
• Comfort using AI/LLM tools to accelerate causal inference workflows, automate repetitive analysis, or scale experimentation education content.
• Passion for Discord or gaming.
• Causal inference-related publications.

San Francisco Bay Area:

Candidates must reside in or be willing to relocate to the San Francisco Bay Area (Alameda, Contra Costa, Marin, Napa, San Francisco, San Mateo, Santa Clara, Solano, and Sonoma counties). Relocation assistance may be available.

The US base salary range for this full-time position is $279,000 to $310,000 + equity + benefits. Our salary ranges are determined by role and level. Within the range, individual pay is determined by additional factors, including job-related skills, experience, and relevant education or training. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include equity, or benefits.