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 aData Scientistwith expertise inexperimentation, causal inference, and retention analyticsto 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 inPySparkis highly desirable to handle large-scale datasets efficiently.

About the Role

• Experimentation & Causal Inference

  • Design, implement, and analyzeA/B tests, multi-armed bandits, and quasi-experimental methodsto measure the impact of product changes.

  • Applycausal 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 definehypotheses, success metrics, and statistical power requirements.

  • Ensurerigorous statistical validity(e.g., controlling for biases, multiple testing corrections, confidence intervals).

• Retention & Engagement Analytics

  • Develop and refineretention measurement frameworks(e.g., cohort analysis, survival analysis, churn prediction).

  • Define and trackcore engagement metrics(DAU, WAU, MAU, rolling retention, N-day retention) and diagnose trends.

  • Identifykey drivers of retentionthrough segmentation, funnel analysis, and predictive modeling.

  • Work with growth teams tooptimize onboarding, engagement loops, and monetization strategies.

• Data Infrastructure & Scalable Analytics

  • Build and maintainscalable data pipelines(usingPySpark, SQL, or big data tools) to process and analyze large datasets.

  • Developautomated dashboards and reports(e.g., Tableau, Looker, Metabase) to monitor experiment performance and retention trends.

  • Ensuredata quality and consistencyin metric definitions across teams.

  • Optimize queries and computations forperformance and cost efficiencyin distributed systems (e.g., Databricks, AWS EMR, GCP BigQuery).

• Cross-Functional Collaboration

  • Partner withproduct managers, engineers, and marketersto translate business questions into data-driven analyses.

  • Present findings and recommendations toexecutive stakeholdersin clear, actionable formats.

  • Mentor junior data scientists and analysts onbest practices in experimentation and retention analytics.

You’ll have

• 3+ yearsof experience indata science, analytics, or experimentation(or equivalent in academic research).

• Strong background instatistics and causal inference(hypothesis testing, Bayesian methods, experimental design).

• Hands-on experience withSQLandPython(Pandas, NumPy, SciPy, StatsModels, Scikit-learn).

• Proficiency inexperimentation tools(e.g., Optimizely, Statsig, Eppo, or custom in-house systems).

• Experience defining and analyzingretention metrics(DAU/WAU/MAU, cohort retention, churn).

• Familiarity withbig data tools(PySpark, Hadoop, or similar distributed computing frameworks).

Highly Desirable:

• Expertise in PySparkfor large-scale data processing and analytics.

• Experience withtime-series forecasting, survival analysis, or uplift modeling.

• Knowledge ofML for retention(e.g., propensity models, clustering, recommendation systems).

• Experience withdata visualization tools(Tableau, Looker, Plotly, Matplotlib/Seaborn).

• Background ingrowth analytics, product analytics, or marketing analytics.

Nice to Have:

• Advanced degree (MS/PhD) inStatistics, Economics, Computer Science, or a quantitative field.

• Experience withreinforcement learning or bandit algorithmsfor dynamic experimentation.

• Knowledge ofMLOps or productionizing models(e.g., MLflow, Airflow, Docker).

What we offer

• We offer competitive compensation and equity aligned to the markets where our team members are based. The base salary range will depend on the candidate’s permanent work location.

• Comprehensive health and wellness benefits, including medical, dental, vision, and additional support programs.

• The opportunity to work on cutting-edge AI with a small, mission-driven team

• A culture that values transparency, trust, and community impact

Come help build the space where anyone can explore and help shape the future of AI.

Arena Intelligenceprovides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, genetics, sexual orientation, gender identity, or gender expression. We are committed to a diverse and inclusive workforce and welcome people from all backgrounds, experiences, perspectives, and abilities.