Data Science Talent Demand in China: A Large-Scale Job Posting Analysis and Implications for Curriculum Alignment

The rapid expansion of the digital economy has intensified demand for data science talent in China, yet higher education curricula often lag behind evolving industry requirements. While the skills gap is widely acknowledged, few studies offer large-scale empirical evidence linking labor-market signals to curriculum design in the Chinese context. This study analyzes 12,436 data science–related job postings from major Chinese recruitment platforms between 2022 and 2024 to map employer demands and their educational implications. Using text preprocessing, natural language processing, skill extraction, clustering, and regression techniques, we identify key patterns in geographic distribution, required competencies, and salary drivers. Results show that job demand is heavily concentrated in Tier-1 and Tier-2 cities. The most frequently required skills include Python, SQL, machine learning, big data tools (e.g., Spark and Hadoop), statistical analysis, and communication abilities. Salaries are most strongly influenced by city tier, company size, educational qualifications, and proficiency in specialized technical areas such as cloud platforms and deep learning. A notable mismatch persists between university training and market expectations—particularly in applied technical skills and interdisciplinary problem-solving. These findings provide an evidence-based foundation for curriculum redesign, stronger industry–academia collaboration, and more responsive educational planning. Future research should extend to longitudinal forecasting and cross-country comparisons.

data science talent demand; job posting analytics; curriculum alignment; China

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