About the role
About Arena Intelligence Arena Intelligence is the open platform for evaluating how AI models perform in the real world. Created by researchers from UC Berkeley’s SkyLab, our mission is to measure and advance the frontier of AI for real-world use. Millions of people use Arena Intelligence each month to explore how frontier systems perform — and we use our community’s feedback to build transparent, rigorous, and human-centered model evaluations. Leading enterprises and AI labs rely on our evaluations to understand real-world reliability, alignment, and impact. Our leaderboards are the gold standard for AI performance — trusted by leaders across the AI community and shaping the global conversation on model reliability and progress. We’re a team of researchers, engineers, academics, and builders from places like UC Berkeley, Google, Stanford, DeepMind, and Discord. 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. Everyone on our team is a deep expert in their field — our office radiates excellence, energy, and focus.
About
the Role As a Data Scientist you’ll explore and reason about the data that powers millions of AI evaluations each week. You’ll generate and test hypotheses, identify causal relationships, and uncover insights that help us understand how frontier models behave in the real world. You’ll collaborate with ML researchers and engineers to design experiments, analyze large-scale datasets, and build statistical frameworks that improve the reliability and interpretability of our AI evaluation systems. We are considering candidates who are senior level or higher for this role. You’ll Explore and analyze large, complex datasets to uncover patterns, biases, and causal relationships in model behavior and system performance. Formulate hypotheses about data quality, evaluation outcomes, and model performance — then design experiments to validate or refute them. Build reproducible analysis pipelines using Python, Pandas, NumPy, and Spark to process and interrogate large-scale data. Partner with ML researchers and engineers to design metrics and analyses that evaluate how models perform across domains, prompts, and tasks. Develop causal reasoning frameworks and statistical methods that help explain why models behave as they do — not just how well they perform. Communicate insights (for example, via blog posts) clearly to technical and non-technical partners, informing both research direction and infrastructure improvements. You’ll have 6+ years of experience in data science, ML analytics, or applied research, preferably in AI, ML, or large-scale data environments. Strong proficiency in Python, with deep experience in Pandas, NumPy, and distributed frameworks like Spark. Expertise in statistical modeling, causal inference, and experimental design. Experience reasoning about data distributions, sample quality, and the effects of data distribution shifts. Strong communication skills and the ability to collaborate closely with ML researchers and engineers. (