AVP/Senior Associate, Data/Business Analytics (Risk and Fraud Analytics under Data Chapter)
- Hong Kong
- Permanent
- Full-time
- Risk Domain Expertise: Apply a solid understanding of credit and/or fraud risk management principles, regulatory requirements, and industry best practices to all analytical endeavors.
- Model Development & Implementation: Design, develop, validate, and deploy statistical and machine learning models for credit risk assessment, fraud detection, and other related risk analytics applications. This includes data exploration, feature engineering, model selection, training, testing, and performance monitoring.
- Advanced Analytics & Problem Solving: Apply quantitative and analytical techniques to complex business problems, providing actionable insights and data-driven recommendations to improve risk management strategies and operational efficiency.
- Machine Learning & Generative AI Exploration: Actively explore, experiment with, and potentially develop hands-on solutions utilizing advanced Machine Learning (ML) and/or Generative AI (Gen AI) techniques within the risk domain, identifying opportunities for innovation and enhanced model performance.
- Data Strategy & Technical Proficiency: Work with large, complex datasets, demonstrating strong technical skills in programming languages (e.g., Python, Spark, SQL) and data manipulation/modeling tools.
- Collaboration & Communication: Collaborate closely with cross-functional teams including risk managers, product owners, engineers, and business stakeholders. Clearly articulate complex analytical concepts, model methodologies, and findings to both technical and non-technical audiences through effective presentations and written reports.
- Experience: 5-7 years of hands-on professional experience in data science, quantitative analytics, or a related field, with a strong focus on credit risk analytics and/or fraud risk analytics within a financial services or relevant industry.
- Risk Modelling Expertise: Solid understanding of risk modelling concepts, statistical modeling, and machine learning algorithms (e.g., logistic regression, decision trees, gradient boosting, neural networks).
- Quantitative & Analytical Acumen: Strong quantitative and analytical skills with the ability to translate complex data into clear, actionable insights.
- Technical Proficiency:
- Proficiency in programming languages commonly used for data science (e.g., Python).
- Experience with relevant data science libraries (e.g., scikit-learn, pandas, numpy, TensorFlow, PyTorch).
- Experience with big data platforms (e.g., Hadoop, Spark) is a plus.
- ML/Gen AI Interest: Demonstrated hands-on experience or a strong, proven interest in developing solutions using Machine Learning and/or Generative AI in a risk-related context.
- Communication & Presentation Skills: Excellent verbal and written communication skills, with the ability to articulate complex technical concepts and findings clearly and concisely to diverse audiences. Proven ability to create and deliver impactful presentations.
- Problem-Solving: Exceptional problem-solving capabilities with a structured and logical approach to tackling complex business challenges.
- Education: A degree in a quantitative field such as Data Science, Statistics, Mathematics, Computer Science, Economics, Operations Research, or a related discipline is generally preferred. We value practical experience and a strong aptitude for learning as much as formal qualifications.