Quantitative Risk & Analytics Consultant

- Advise financial institutions on the design, enhancement and implementation of risk management and management control frameworks.
- Support clients in improving quantitative risk measurement methodologies, analytics capabilities and governance processes to facilitate informed business decision-making.
- Provide consulting support on regulatory and supervisory expectations related to quantitative models, model governance, documentation and operational frameworks.
- Develop, enhance and validate quantitative models, including valuation models, risk measurement models and data analytics models.
- Gather and analyse business requirements from client stakeholders and translate them into effective analytical and modelling solutions.
- Apply advanced quantitative techniques, financial engineering methods, data analytics, AI and machine learning approaches to address complex business and risk challenges.
- Advise clients on the practical application and integration of models into business and risk management processes.
- Support corporate clients in strengthening management strategies and decision-making through quantitative analysis and risk-informed approaches.

Employee Benefits:
Social insurance and statutory benefits in accordance with Japanese law. Additional benefits are available and can be discussed during the recruitment process.

Essential:
- Experience using quantitative methods within a major financial institution, such as:
1) Quantitative research (Quant)
2) Risk management
3) Financial risk analytics
4) Related quantitative functions
5) OR experience conducting quantitative analysis within a corporate environment.
- Strong quantitative modelling, statistical analysis or financial engineering capabilities.
- Interest in applying technical expertise within a consulting or advisory environment.
- Ability to communicate analytical findings and recommendations to stakeholders.

Preferred:
- Experience in consulting, advisory or professional services environments.
- Experience supporting model development, model validation, risk measurement or regulatory-related initiatives.
- Knowledge of AI, machine learning or advanced data analytics techniques applied to business or risk management challenges.