Summary

Responsibilities

1. Data Architecture & Infrastructure

1. Review and refactor existing data table structures, field semantics, and key/ID systems to resolve legacy issues such as “one field with multiple meanings”
2. Design and drive a unified data model and metric definitions; establish a company‑wide data dictionary and data standards
3. 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

1. Own the overall methodology and implementation path for A/B testing and other online experiments across the company
2. Design experiment pipelines, including traffic allocation, tracking/instrumentation strategy, data collection, data storage, and analysis workflows
3. Develop standardized experiment analysis frameworks and reusable templates, including core metrics, significance testing, sample size estimation, and evaluation guidelines

3. Business Partnership & Decision Support

1. Deeply engage with core business lines and define problems and key metrics starting from product and business goals
2. Based on the unified data system, provide structured data analysis and experiment recommendations to product and business teams
3. 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

1. Promote data naming conventions, field definitions, and tracking standards, and ensure their adoption across the company
2. Establish and maintain data quality monitoring mechanisms to detect and fix data issues
3. 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

1. Bachelor’s degree or above in Computer Science, Statistics, Mathematics, Information Engineering, or related fields
2. 3+ years of experience as a Data Scientist, Data Product Manager, Data Engineer, or Growth Analyst
3. Experience building a data system from scratch or leading large‑scale data infrastructure re‑architecture is a strong plus

2. Technical Skills

1. Strong SQL skills: capable of handling complex joins and large‑scale queries with attention to performance and maintainability
2. Proficient in Python (or a similar language) for data cleaning, analysis/modeling, and developing automated analysis scripts
3. Solid understanding of data warehouse modeling concepts (e.g., dimensional modeling, star/snowflake schemas) and data architecture
4. Familiar with online experimentation (A/B testing), including metric design, experiment design, statistical testing, and sample size estimation

3. Business & Communication Skills

1. Proven experience working closely with business teams and translating business problems into measurable, testable data and experiment questions
2. Strong communication skills; able to collaborate effectively with product, business, and engineering stakeholders
3. Highly self‑driven, with a strong sense of ownership for “making data and experimentation work well at the company” beyond just completing ad‑hoc analysis tasks

Nice to Have

1. End‑to‑end experience building an experimentation platform, metrics platform, or data governance framework at the company level
2. Experience in Internet / SaaS / consumer products, with practical work on user behavior analysis, funnel analysis, and retention analysis
3. Familiarity with common statistical modeling or machine learning methods, with hands‑on experience in use cases such as recommendation, pricing, ranking, or user segmentation

Skills

• Analytical Thinking
• Attention to Detail
• Development
• Growth Strategy
• Product Management
• Python
• Software Engineering
• SQL