
Responsibilities
Own the definition and evolution of success metrics for all user facing surfaces, including tradeoffs between short-term and long-term member value. Define and maintain business metrics, semantic layers, and data models to ensure consistency and trust across the Places team. Own, in partnership with Data Engineering, the translation of product requirements into scalable, reliable data assets that empower efficient decision-making Influence product direction by uncovering and translating insights into clear recommendations that shape roadmap prioritization. Build out a culture of high fidelity experimentation to scale wins quickly Using visualisations to convey nuance with clarity AI-native mindset and hands-on experience using LLMs, agents, and modern AI tools to automate analysis, accelerate experimentation, and build scalable decision-support workflows
Qualifications
5+ years of data science and product analytics experience BS and/or MS in a quantitative discipline: statistics, operations research, computer science, engineering, applied mathematics, physics, economics, etc. Familiarity in delivering data products through partnership with Data and product engineering teams. Strong technical fluency with analytical tools such as SQL and Python, or equivalent experience using data to answer open-ended business questions. Strong applied statistics background, including hypothesis testing, confidence intervals, power analysis, and causal inference techniques. Familiarity with modern analytics and experimentation tools Familiarity with consumer focused . Strong in proactive verbal and written communication, ability to convey rigorous statistical concepts to non-experts. Practical AI fluency and curiosity about how modern AI tools can improve analytical workflows, strategic synthesis, and decision support. Bonus points for familiarity with optimization of performance marketing efforts