Data Analyst

Candidates must have legal eligibility to work in the U.S. and demonstrate Chinese language proficiency.

需持有美国工作签证或PR，中文流利，美国全域可远程办公

Key Responsibilities

• Data Analysis: Conduct deep-dive analyses for programmatic advertising modules; use data mining and modeling (where applicable) to identify business problems and growth opportunities, and translate findings into actionable recommendations.

• Data Extraction & Modeling: Use SQL and Python for complex data extraction and processing; help build and maintain reliable data models and metric systems for ad serving and monetization.

• Insight Visualization & Reporting: Design and develop business monitoring dashboards; produce regular analysis reports that present insights to leadership and business stakeholders.

• Support Business Decisions: Partner with product, operations, and algorithm teams to translate analytical findings into business actions, track results, and support the decisionmaking loop.

• Data Quality Monitoring: Own quality checks and monitoring for key advertising and business data to ensure accuracy and consistency.

Qualifications

Hard Skills:

• Bachelor's degree or above in Statistics, Computer Science, Mathematics, Information Management, or other STEM-related fields

• Strong SQL skills (multi-table queries, performance optimization, implementing business logic)

• Strong Python skills (Pandas, NumPy) for data cleaning, analysis, and processing —familiarity with Scikit-learn is a plus

• Basic familiarity with Spark for large-scale data processing is a plus, not a requirement

• Proficient in Excel (PivotTables, VLOOKUP, etc.)

• Hands-on experience with at least one visualization tool (e.g., Tableau, Power BI)

Soft Skills & Experience:

• 1–3 years of commercial data analysis experience; programmatic advertising, ad tech, or internet industry experience is preferred

• Strong logical reasoning and business acumen; able to turn ambiguous business questions into clear analytics problems

• Self-driven and solution-oriented; proactive in spotting issues behind the data

• Strong communication and cross-team collaboration skills

• Detail-oriented, with a strong sense of ownership and data quality