Operational Risk Modeller

Job Description & Summary:

Designation - Senior Associate / Manager

Location - Mumbai/Bangalore

PwC India is seeking an experienced Operational Risk Modeller to develop/implement/validate advanced risk models for a large multinational bank.

Risk Modelling: Develop, refine, and implement advanced operational risk models using quantitative techniques to evaluate the potential impacts of various risk scenarios. Examples include:

Loss Distribution Approach (LDA): Utilize historical loss data to estimate potential future losses by fitting statistical distributions to loss severity and frequency.

Scenario Analysis Models: Construct models that incorporate hypothetical scenarios and stress tests to assess the impact of extreme events, such as cyber attacks or major compliance breaches.

Monte Carlo Simulations: Execute Monte Carlo simulations to generate a range of possible outcomes and assess the likelihood and impact of different risk scenarios.

Data Analysis: Collect, clean, and analyze large datasets to identify trends, patterns, and anomalies that could indicate emerging operational risks, using industry-standard tools such as SAS, Python, or R.

Model Validation: Conduct regular validation and back-testing of risk models, including sensitivity analysis and benchmarking, to ensure accuracy, reliability, and compliance with industry regulations such as Basel II/III.

Risk Assessment: Collaborate with cross-functional teams to translate model outputs into actionable insights, influencing risk management strategies and decisions, particularly in areas like fraud prevention, business continuity, and regulatory compliance.

Documentation: Maintain comprehensive documentation of all modelling processes, methodologies, assumptions, and results, adhering to audit and regulatory standards.

Reporting: Prepare detailed risk reports for senior management and regulatory bodies, communicating complex findings clearly and concisely, using visualization tools such as Tableau.

Continuous Improvement: Stay informed about current industry practices and innovations, adapting models and techniques to mitigate evolving operational risks, and participate in industry forums to exchange knowledge on risk modelling.

Mandatory skill sets:

Quantitative Analysis: Proficiency in statistical and quantitative techniques relevant to operational risk modelling. Software Proficiency: Advanced skills in programming languages such as R, Python, or SAS, and experience with data visualization tools such as Tableau or Power BI. Problem Solving: Strong analytical and problem-solving skills with a focus on accuracy and attention to detail.