Data & Applied Scientist II

Experimentation & A/B Testing Design, analyze, and interpret A/B experiments end‑to‑end, from hypothesis formulation to final decision Choose appropriate metrics, success criteria, and evaluation windows based on user behavior and business context. Identify and diagnose common experimentation issues (e.g., bias, interference, power limitations, metric sensitivity). Communicate experimental results clearly, including uncertainty, limitations, and trade‑offs. Develop good judgment about when to rely on automation vs. when deep statistical reasoning is required. Contribute to shared standards and documentation that improve how teams run experiments and make decisions. Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) or consulting experience OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 2+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience. Experimentation & A/B Testing Demonstrated experience designing, analyzing, and interpreting A/B experiments end‑to‑end. Solid understanding of experimental design concepts, including hypotheses, control/treatment comparisons, metrics, and evaluation windows. Ability to identify and reason about common experimentation challenges such as bias, interference, insufficient power, and metric sensitivity. Experience communicating experimental results clearly, including uncertainty, limitations, and trade‑offs. Ability to work with real‑world data that is noisy, incomplete, or imperfect, and still produce reliable insights Experience using AI‑assisted tools to support data analysis, experimentation, or insight generation. Ability to thoughtfully integrate AI into everyday analytical workflows while maintaining statistical rigor. Proficiency in SQL for data extraction and analysis. Experience with at least one analytical programming language (e.g., Python or R). Familiarity with experimentation analysis workflows, dashboards, or analytical tooling. Ability to explain complex analytical concepts and experimental results to non‑technical audiences. Experience working cross‑functionally with product, engineering, or design partners.