Job Responsibilities:
- Build core data models covering the full user lifecycle, including LTV prediction, churn risk forecasting, payment propensity, and conversion probability modeling. Develop refined user profiling and segmentation frameworks to identify high-potential and high-value user groups, and translate insights into actionable monetization and growth strategy recommendations for product and operations teams.
- Conduct in-depth data attribution and impact analysis on product features, operational campaigns, and key business strategies, quantifying their effects on user behavior, retention, monetization, and overall revenue to support data-driven decision-making.
- Design and lead A/B testing and other causal experiments, establish robust and scientific evaluation metrics, and systematically assess the true incremental impact of product iterations and strategic adjustments.
Requirements:
- Master's degree or above in Statistics, Computer Science, Mathematics, Economics, or a related field, with at least 3 years of experience in data science, machine learning, or advanced data analytics.
- Strong proficiency in Python (e.g. Pandas, Scikit-learn, XGBoost / LightGBM) and SQL, with hands-on experience handling large-scale datasets (hundreds of millions of records).
- Solid foundation in statistics with a deep understanding of common machine learning algorithms (classification, regression, clustering), and familiarity with causal inference methodologies.
- Strong data-driven mindset and business acumen, with the ability to translate complex business problems into quantitative models and convert analytical results into actionable business strategies.
Preferred Qualifications (Nice to Have):
- Background in the internet industry, with hands-on experience in commercialization (advertising / games / e-commerce) or user growth optimization will be an advantage.
