Data Analyst, Risk

About the Role

We are looking for a highly motivated and detail-oriented Data Analyst to join our team. In this role, you will be responsible for building and maintaining user tagging systems, developing user profiles, and generating actionable insights from large-scale user data. You will work closely with cross-functional teams to support personalized services, risk control strategies, and data-driven decision-making across the organization.

Responsibilities

• Manage and maintain a comprehensive user tagging system to support personalized services and risk control initiatives.
• Analyze user behavior data to build accurate, dynamic, and actionable user profiles.
• Perform data mining, feature engineering, and exploratory analysis to extract meaningful insights from large-scale datasets.
• Collaborate closely with cross-functional teams, including Product, Risk, Marketing, and Technology, to optimize user segmentation and targeting strategies.
• Monitor and evaluate the effectiveness of user tags and user profiles, continuously improving data quality, accuracy, and relevance.
• Support the implementation of data-driven decision-making processes across the organization.
• Identify patterns, anomalies, and emerging trends in user behavior to support business growth and risk mitigation.
• Contribute to the continuous improvement of data methodologies, profiling frameworks, and analytical processes.

Requirements

Bachelor’s degree or above in Data Science, Statistics, Computer Science, Mathematics, Economics, or a related field.
Minimum 3–5 years of experience in data analysis, user profiling, customer segmentation, or related areas.
Strong background in data analytics, user behavior analysis, and customer segmentation.
Proficiency in Python, SQL, and data visualization tools such as Tableau or Power BI.
Experience with big data platforms and related tools is a plus.
Solid understanding of user tagging methodologies and user journey mapping.
Familiarity with black-market ecosystems and the operating models of bonus abuse, promotion abuse, or fraudulent studio groups is highly preferred; hands-on experience in identifying and combating such activities is a strong advantage.
Knowledge of machine learning techniques and experience applying them to user data is an advantage.
Excellent analytical thinking and problem-solving skills.
Ability to work collaboratively in a fast-paced, international team environment.
Detail-oriented, with a strong sense of data accuracy, consistency, and integrity.
Experience with AI technologies and practical applications is a plus.
Preferred Qualifications
Experience in internet platforms, fintech, risk control, growth analytics, or anti-fraud related domains.
Familiarity with experimentation frameworks, A/B testing, and user lifecycle analysis.
Experience working with large-scale behavioral datasets and real-time analytics scenarios.

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