About the role
About Arena Intelligence Arena is the platform for evaluating how AI models perform in the real world. Founded by researchers from UC Berkeley's SkyLab, we're on a mission to measure and advance the frontier of AI for real-world use, and to build the foundation for everyone to understand, shape, and benefit from it. Tens of millions of people use Arena each month to evaluate how frontier systems handle the work they actually do. The preferences they share power the most transparent, rigorous, and human-centered evaluations in AI. Leading AI labs, enterprises, and independent researchers rely on our work and open datasets to understand how models behave in real workflows: agentic coding, creative generation, professional productivity, and beyond. We go beyond leaderboards and decompose what human experience reveals about AI, so models advance toward the work people actually do. We're a team of researchers, academics, builders, and creatives from UC Berkeley, Google, Stanford, and DeepMind. We seek truth, move fast, and value craftsmanship, curiosity, and impact over hierarchy. We're building a company where thoughtful, curious people from all backgrounds can do their best work together, in an office culture that radiates excellence, energy, and focus.
About
the Role We are seeking a Data Scientist with expertise in experimentation, causal inference, and retention analytics to drive data-informed decision-making and optimize user engagement. In this role, you will design and analyze experiments (A/B tests, quasi-experiments), develop measurement frameworks for key metrics (DAU, WAU, MAU, retention), and provide actionable insights to improve product growth and user retention. Proficiency in PySpark is highly desirable to handle large-scale datasets efficiently.
About
the Role Experimentation & Causal Inference Design, implement, and analyze A/B tests, multi-armed bandits, and quasi-experimental methods to measure the impact of product changes. Apply causal inference techniques (e.g., difference-in-differences, propensity score matching, synthetic control, regression discontinuity) to estimate treatment effects in non-randomized settings. Collaborate with product, engineering, and marketing teams to define hypotheses, success metrics, and statistical power