Responsibilities

📍1. Data Architecture & Infrastructure 📍Review and refactor existing data table structures, field semantics, and key/ID systems to resolve legacy issues such as “one field with multiple meanings” 📍Design and drive a unified data model and metric definitions; establish a company‑wide data dictionary and data standards 📍Partner closely with engineering to participate in data warehouse modeling and data pipeline (ETL/ELT) design, improving data quality and maintainability 📍2. Experimentation System & Evaluation Framework 📍Own the overall methodology and implementation path for A/B testing and other online experiments across the company 📍Design experiment pipelines, including traffic allocation, tracking/instrumentation strategy, data collection, data storage, and analysis workflows 📍Develop standardized experiment analysis frameworks and reusable templates, including core metrics, significance testing, sample size estimation, and evaluation guidelines 📍3. Business Partnership & Decision Support 📍Deeply engage with core business lines and define problems and key metrics starting from product and business goals 📍Based on the unified data system, provide structured data analysis and experiment recommendations to product and business teams 📍Use data and experiment results to identify growth opportunities, product optimization directions, and potential risks, and clearly communicate findings to non‑technical stakeholders 📍4. Data Governance & Best Practices 📍Promote data naming conventions, field definitions, and tracking standards, and ensure their adoption across the company 📍Establish and maintain data quality monitoring mechanisms to detect and fix data issues 📍Document and socialize internal best practices related to data and experimentation, helping to build a strong data culture and improve overall data usage efficiency

Requirements

📍1. Education & Experience 📍Bachelor’s degree or above in Computer Science, Statistics, Mathematics, Information Engineering, or related fields 📍3+ years of experience as a Data Scientist, Data Product Manager, Data Engineer, or Growth Analyst 📍Experience building a data system from scratch or leading large‑scale data infrastructure re‑architecture is a strong plus 📍2. Technical Skills 📍Strong SQL skills: capable of handling complex joins and large‑scale queries with attention to performance and maintainability 📍Proficient in Python (or a similar language) for data cleaning, analysis/modeling, and developing automated analysis scripts 📍Solid understanding of data warehouse modeling concepts (e.g., dimensional modeling, star/snowflake schemas) and data architecture 📍Familiar with online experimentation (A/B testing), including metric design, experiment design, statistical testing, and sample size estimation

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