Data Scientist

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.

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.

• 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.

• 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.

• (Bonus) Background in AI model evaluation.

• (Bonus) Experience working with LLM outputs (for example, LLM-as-a-judge), embeddings, or other large-scale model artifacts.

• (Bonus) Experience with A/B testing.