Data Analyst, Risk
Asia / Taiwan, Taipei / Australia, Brisbane / Australia, Melbourne / Australia, Sydney / Hong Kong / South East AsiaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 3–5 years
- Required Skills
- PythonSQLData AnalysisData MiningMachine LearningMicrosoft Power BITableau
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.
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.
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