Nova Credit Limited
We are looking for a seasoned modeler to join our modelling team. The ideal candidate is experienced in data mining/data analysis/model development on large data. Experienced in using a variety of analytics tools. The right candidate will have a passion for discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes.
Principal responsibilities
• Conduct data mining from big data mart including financial data and alternative data
• Develop and validate credit scorecards and other predictive models across retail/SME risk
• Write document with findings, make recommendations, and communicate the results to management and stakeholders
• Coordinate with different functional teams to implement models and monitor outcomes
• Develop processes and tools for model development and monitoring
• Support in ad hoc tasks / project as required
• University degree in Statistics, Mathematics, Computer Science, Financial Engineering or other quantitative fields;
• Minimum of three years’ experience in statistical model development;
• Strong problem solving skills, strong self-motivated and able to meet tight deadlines and make decisions independently as well as a good team player
• Experience using statistical computer languages (SAS, R, Python, etc.) to analyze data and develop models.
• Knowledge of advanced statistical techniques and concepts and experience with applications.
• Knowledge of a variety of machine learning techniques.
• Strong written and verbal communication skills and presentation skills in Cantonese, Mandarin and English; Strong project management skills;
• A drive to learn and master new technologies and techniques.
Email CV to hr@nova-credit.com, stating present (if any) and expected salary.
Suitable candidate will be contacted for interview.
All personal data provided will be treated in strict confidence and used for recruitment purpose only. It is our policy to retain the personal data of unsuccessful applicants for future recruitment purposes for a period of two years.