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An established industry player is seeking a Risk Analyst to join their Risk and Compliance team. This role offers the chance to engage in quantitative modelling and analytics, focusing on financial risk and compliance. As you contribute to stress testing and sensitivity analysis, you'll enhance your skills in R and Python coding while gaining valuable experience in a dynamic environment. With a commitment to employee development, this position provides opportunities for growth and learning in the SME finance sector. Join a supportive team and make a meaningful impact on risk management strategies.
Application Deadline: 17 April 2025
Department: Risk
Employment Type: Permanent
Location: London
Compensation: £26,250 - £35,438 / year
You will be joining the Risk and Compliance team undertaking a range of quantitative modelling and analytics to support the measurement, monitoring and management of risk, including Financial Risk models, Stress Testing, Sensitivity Analysis and a range of IFRS9 ECL modelling and reporting across our portfolios. As a Risk Analyst you will assist in our quantitative activities across our existing portfolios and any new programmes we may implement, drawing on your knowledge and experience in both R and Python coding. You will have the opportunity to gain extensive practical experience working on various quantitative modelling and analytical initiatives, progressively taking on greater responsibility which will enable you to develop your skills in a dynamic and supportive setting.
You must have knowledge and experience of R and Python coding languages as a prerequisite for this role, with a willingness to develop these skills further. Knowledge of other coding languages would be beneficial.
You should have a proficient understanding of statistical concepts and methodologies, including Ordinary Least Squares (OLS), Logistic Regression, Generalised Linear Models (GLM), Principal Component Analysis (PCA), knowledge of key statistical distributions, simulations and bootstrapping. Familiarity with time series models such as ARIMA, as well as hypothesis testing (e.g., t-tests, chi-square tests), ANOVA, and their key statistical assumptions and tests is essential.
You will be able to demonstrate proficient analytical skills and attention to detail and ideally have experience/understanding of credit or investment risk management in the public or private sector.
You should also possess technical skills in Excel and PowerPoint, with PowerBI being beneficial, and have an interest in the SME finance marketplace with an eagerness to learn more about the sector.