- Design and develop quantitative risk measurement models using advanced data science and statistical techniques.
- Utilize large datasets to build predictive models for assessing various risk factors, including credit risk, market risk, liquidity risk, and operational risk.
- Collaborate with cross-functional teams to gather, clean, and validate data inputs for model development.
- Implement machine learning algorithms, statistical methodologies, and data visualization tools to analyze and interpret model results.
- Validate and test risk models to ensure their accuracy, robustness, and compliance with regulatory requirements.
- Stay updated on emerging data science and machine learning trends and adapt model methodologies accordingly.
- Collaborate with Risk Management and IT teams to integrate and deploy risk models into operational processes.
- Prepare comprehensive documentation of model development and validation processes.
- Assist in model monitoring, maintenance, and periodic model reviews.
- Contribute to the development of risk model governance policies and procedures.
Qualifications:
- Proven experience in Quantitative Modeling and Data Science, with a focus on Risk Measurement.
- Strong programming skills in languages such as Python, R, or MATLAB.
- Proficiency in machine learning techniques, statistical analysis, and data visualization.
- Familiarity with data manipulation, data preprocessing, and feature engineering.
- Knowledge of financial products, risk factors, and regulatory requirements.
- Excellent problem-solving and critical-thinking abilities.
- Effective communication skills to explain complex model concepts to non-technical stakeholders.
- Experience with risk modeling software and version control tools is a plus
- Bachelor's, Master's, or Ph.D. in a quantitative field such as Statistics, Mathematics, Data Science, Finance, or a related discipline.
If you are interested, please share your updated resume and suggest the best number & time to connect with you.