Description

Risk Modelling & Analytics (Insurer) Overview
We are seeking a highly analytical professional to support the development and enhancement of quantitative risk models across a range of financial and operational risk areas. This role focuses on leveraging advanced analytics to generate insights, strengthen risk management practices, and support data-driven decision-making.

The role also involves collaboration with internal and external stakeholders to explore innovative solutions, including the application of advanced analytics and AI tools, to improve efficiency in risk and compliance processes.

Key Responsibilities

Risk Modelling & Analytics

1. Develop, maintain, and enhance quantitative models used to assess a broad range of risks, including scenario-based models, balance sheet and asset-liability models, liquidity and funding risk models, and credit risk models.
2. Perform regular model validation, performance monitoring, and benchmarking to ensure robustness and reliability.
3. Document model methodologies, assumptions, and outputs in accordance with internal governance standards.

Risk Innovation & Data Analytics

1. Apply statistical and machine learning techniques to identify trends, detect anomalies, and improve risk monitoring.
2. Support initiatives involving advanced analytics or AI tools to enhance risk and compliance processes.
3. Explore new methodologies to strengthen predictive and analytical capabilities.

Stakeholder Management & Communication

1. Collaborate with cross-functional teams to understand business needs and incorporate insights into analytical solutions.
2. Translate complex analytical findings into clear, actionable insights for both technical and non-technical stakeholders.
3. Provide data-driven recommendations to support strategic decision-making and risk mitigation.

Requirements

1. Degree in a quantitative discipline such as Mathematics, Statistics, Economics, Finance, or Data Science.
2. Proficiency in Python, R, or similar tools for statistical modelling and data analysis.
3. Working knowledge of SQL for data extraction and manipulation.
4. Familiarity with modelling techniques such as regression, time series analysis, and machine learning.
5. Basic understanding of risk management principles and regulatory environments.
6. Strong analytical thinking, attention to detail, and problem-solving skills.
7. Effective communication and interpersonal skills.
8. Ability to work independently and collaboratively in a fast-paced environment.

Preferred:

1. Exposure to financial or actuarial reporting frameworks.
2. Experience with forecasting or advanced modelling tools.
3. Professional certifications in risk, finance, or analytics (e.g., FRM, CFA, actuarial qualifications).

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