Must have

• Python

• Communication skills

• English (C1)

Requirements description

What you need to have to succeed in this role

• Academic background in a quantitative field such as Mathematics, Data Science, Econometrics, Engineering, Physics. Qualification and Expertise in mathematics / statistics / machine learning algorithms.
• Solid background in Python programming, preferably in large scale financial or technical computations.
• 3+ years of relevant experience in analytics preferably in the fields such as Risk Modelling, Stress Testing, Forecasting etc. preferably for a Banking organization
• Open personality and effective communication skills, including experience speaking to technical and business audiences and working globally.
• Good understanding of the Banking Book risks: IRRBB risk components and liquidity risks - nice to have.
• Experience in designing behavioural models - nice to have.
• Familiarity with financial assets and liabilities, with a preference for experience with asset pricing and metrics used to govern financial institutions, e.g. liquidity coverage ratios, balance sheet and capital ratios - nice to have.
• Experience with financial markets, with a preference for vanilla interest rate derivative pricing, bond products pricing, curve construction, hedging strategies and risk management. Strong understanding of banking products such as Deposits, Savings, Lending, Credit Cards - nice to have.

Offer description

Your career opportunity

Global Finance aspires to be the best finance function in the financial services industry, delivering powerful insights to our customers, within the bank and externally. We work as a seamless team to support HSBC’s businesses to deliver our strategy.  This role is focused on building new, future-proof modelling solutions.

If your CV meets our criteria, you should expect the following steps in the recruitment process:

• Online behavioural
• Telephone screen
• Job interview with the hiring manager

Your responsibilities

1. Use statistical modelling techniques to develop liquidity risk models
2. Develop the required behavioural models for different products to assess the IRRBB risk metrics.
3. se statistical modelling techniques to develop prepayment/pipeline models for mortgage products to hedge the risk and assess the IRRBB risk metrics.
4. Use the quantitative expertise to design models supporting the Markets Treasury business and other functional Treasury teams where required. Proactively build tools in Python to test the proposed models, to formulate requisite analysis and to measure the impacts of model change.
5. Work together with Financial Engineering and IT teams to contribute in the development of the One Treasury Platform. Be involved, whenever required, in the initiatives related to the other Global Finance Analytics pillars: Forecasting, Flow Analytics and Core calculations. Contribute to the improvement of these models through assessment of impact, model validation, and helping document changes for internal and external use.
6. Understand both regulatory and business requirements, ensuring that the models are fit-for-purpose.

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