# Jiajia Zhou > Data Scientist @ Meta Reality Labs | GenAI | Product Leadership Location: San Francisco, California, United States Profile: https://flows.cv/jiajiazhou AI-native data scientist with 5+ years driving emerging products and business decisions in fast-growing orgs, including Meta Reality Labs and Uber. I work at the intersection of GenAI and wearables/AR—turning ambiguous product questions into clear metrics, evaluation signals, and shipped improvements. My operating beliefs are: 1. AI-first by default: if a workflow can be automated, it should be. Turn messy, manual analysis into scalable systems—using LLMs and lightweight tooling to move faster, raise the quality bar, and keep teams focused on the highest-leverage decisions. 2. User-obsessed: stay close to how people actually use the product. Find organic scenarios, pressure-test PMF, and advocate for what’s genuinely useful—building what users come back for. 3. Storytelling matters: insights don’t land unless they’re understood. I translate complex analysis into crisp narratives, visuals, and decision-ready recommendations so cross-functional teams can act fast. ## Work Experience ### Senior Data Scientist, Wearables @ Meta Jan 2024 – Present | 伯灵格姆, CA AI-native Product Data Scientist owning measurement + evaluation loops for Wearables GenAI/CoreUX—driving phone-replacement adoption, task success, and retention across 10+ product teams. ▸ Defined Phone Replacement Rate as the org-level north star for phone-free high-value tasks (media, capture, comms, navigation); built event taxonomy + logging spec and launch governance, raising coverage 0%→90% for consistent cross-feature measurement. Holdout analysis showed users reaching ≥3 phone-free successful tasks have +5pp D7 retention since activation, informing roadmap priorities. ▸ Built an LLM-judge labeling framework for 15 voice-initiated actions (~50K/week), generating success/failure + failure taxonomy with QC via 3% spot-check + adjudication. Published a recurring quality report as the measurement standard for roadmap decisions, driving root-cause fixes (routing, vocabulary) and boosting task success +20pp. ▸ Led analytics for LiveAI (camera-based multimodal assistant); ran use-case mining + retention analysis across ~3K daily sessions to surface repeat-use scenarios and drive PMF improvements for a major 2025 GTM campaign. Defined latency + reliability metrics and built a monitoring dashboard; insights drove a 2-month launch delay to reach 99% reliability readiness. ▸ Designed pillar-level experimentation governance (hypotheses, success/guardrail metrics, readout standards) across 10 CoreUX & Inputs engineering teams, reducing coordination overhead and preventing cherry-picking. Drove launch decisions across Wearables: 10 launches and 5 pivots. ### Applied Scientist, Policy Research @ Uber Jan 2023 – Jan 2024 | 旧金山湾区 Policy-driven analytics to support high-profile, high-stakes legislative, regulatory, and legal efforts and decision-making, ensuring smooth operations across EMEA, APAC, and NORAM. 𝗣𝗼𝗹𝗶𝗰𝘆 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀: Developed statistical models to assess the impact of urban policies, such as Amsterdam's road closure program, and evaluated Uber's market coverage in the US and UK using both internal and external data sources. 𝗥𝗲𝗴𝘂𝗹𝗮𝘁𝗼𝗿𝘆 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲: Guided Uber's data collection strategy to meet French government requirements and designed automated data solutions for EU DMA legal requests, significantly reducing reporting time. 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 & 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻: ▸ Led a project and presented to CEO Dara Khosrowshahi on behalf of Women@Uber in the Global Safety Think Tank Challenge. ▸ Awarded "Most Innovative" in the Go-Get-Zero 2023 competition for designing a zero-emission future. ### Transportation Engineer I-II, Data Analytics @ Fehr & Peers Jan 2021 – Jan 2022 | 旧金山湾区 Empowered better transportation systems across the Bay Area using data. Key clients included the SF government, real estate developers, and big tech companies. Selected Work: ▸ Conducted causal analysis on the impact of streetscape changes on emergency vehicle travel speed, utilizing over 3 million observational data points. Employed Difference-in-Difference and Regression Discontinuity Design to address confounding variables, enhancing accuracy over conventional regression models. ▸ Applied K-means algorithm to identify 10 clusters across 287 ZIP codes based on residents' commuting patterns, improving model forecasting efficacy. ▸ Developed a web application using Streamlit with an interactive user interface, allowing clients to customize forecast tasks on vehicle miles commuted in response to different policies and commuting programs. ### UI Designer @ Zenport.Inc Jan 2018 – Jan 2018 | Tokyo, Japan ▸ Independently designed and created company website using Figma  ▸ Designed infographics to communicate company mission and user journey ▸ Formulated graphic visualization strategies to optimize marketing/branding effectiveness ▸ Authored the company’s design blog in Medium and promoted Zenport brand ### Information Design Consultant @ MIT Media Lab Jan 2017 – Jan 2017 | Cambridge, MA ▸ Visualized and communicated cluster analysis process grouping different levels of autism, under the direction of Ognjen Rudovic ## Education ### B. A. in Double Majored in Economics and Architecture Boston University ### M. S. in Urban Informatics Rutgers University Edward J. Bloustein School of Planning and Public Policy ### International Exchange Program Keio University ### Design Discovery Summer Program in Architecture Harvard University Graduate School of Design ## Contact & Social - LinkedIn: https://linkedin.com/in/jiajia-jiayu-zhou - Portfolio: https://topmate.io/jiajia_zhou/ --- Source: https://flows.cv/jiajiazhou JSON Resume: https://flows.cv/jiajiazhou/resume.json Last updated: 2026-04-13