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- Tasks: Join our team as a Market Risk Quantitative Analyst, focusing on equity derivatives risk modelling.
- Company: We're a leading investment bank, driving innovation in financial services across Europe and Asia.
- Benefits: Enjoy competitive pay, flexible working options, and opportunities for professional growth.
- Why this job: Be at the forefront of market risk analytics, making impactful decisions in a dynamic environment.
- Qualifications: 5-8 years in quantitative roles, strong skills in SQL and Python, and a solid grasp of market risk models.
- Other info: Ideal for self-starters who thrive in fast-paced settings and want to shape the future of finance.
The predicted salary is between 48000 - 72000 £ per year.
Risk Analytics – Equity market risk quantitative analyst. Our investment banking client is seeking an experienced quantitative analyst / risk modeler with 5 - 8 years of financial industry experience to join the Quantitative Risk team. Focus of this position is on Market Risk modeling for equity derivatives products.
Core Responsibilities:
- Acting as the SME and liaising with front office, technology, and market risk managers to implement and maintain market risk models.
- Making key analytical decisions regarding market risk modelling for Equity derivatives positions traded in Europe and Asia.
- Assessing appropriateness of the market risk model outputs by performing time series review and stationarity test, Basel traffic light backtesting and VaR breaches explanation, P&L attribution test, pricing model benchmark, and quantification of the materiality of any model limitations (e.g. RNIV).
- Documenting model implementation details, tests, and findings for model validation to review, in accordance with Firm’s Model Risk Management policies and framework.
Qualifications:
- Strong background in market risk models and methodologies (e.g. time series analysis, VaR methodologies and backtesting), with 5 - 8 years of previous experience in a quantitative role at a financial institution.
- Good understanding of equity pricing models and products.
- Strong programming skills and data handling skills in SQL and Python (ability to wrangle large data sets, implement statistical tests, and perform data analysis on test results).
- Excellent communication and presentation skills (ability to engage in concise, effective discussions).
- Excellent written skills (ability to produce well-structured technical model documentation).
- Ability to work without significant direct supervision.
- Previous experience of regulatory capital model & economic capital model is preferred.
- Knowledge of Numerix and/or Bloomberg a plus.
Market Risk Quantitative Analytics Consultant (Contract) employer: LevelUP HCS
Contact Detail:
LevelUP HCS Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Market Risk Quantitative Analytics Consultant (Contract)
✨Tip Number 1
Network with professionals in the financial industry, especially those working in market risk and quantitative analytics. Attend industry events or webinars to connect with potential colleagues and learn about the latest trends and challenges in the field.
✨Tip Number 2
Familiarise yourself with the specific market risk models and methodologies mentioned in the job description, such as VaR and time series analysis. Consider brushing up on your skills in SQL and Python, as these are crucial for handling large data sets and performing statistical tests.
✨Tip Number 3
Prepare to discuss your previous experiences in quantitative roles during interviews. Be ready to provide examples of how you've implemented market risk models and the analytical decisions you've made regarding model outputs.
✨Tip Number 4
Showcase your communication skills by practising how to explain complex quantitative concepts in simple terms. This will be essential when liaising with front office teams and presenting your findings to stakeholders.
We think you need these skills to ace Market Risk Quantitative Analytics Consultant (Contract)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in market risk modelling, particularly with equity derivatives. Emphasise your quantitative skills and any relevant programming experience in SQL and Python.
Craft a Strong Cover Letter: In your cover letter, clearly articulate your understanding of market risk models and methodologies. Mention specific projects or experiences that demonstrate your analytical decision-making skills and ability to liaise with various teams.
Showcase Technical Skills: Detail your programming and data handling skills in your application. Provide examples of how you've used SQL and Python for data analysis, especially in the context of financial modelling or risk assessment.
Highlight Communication Abilities: Since excellent communication is crucial for this role, include examples of how you've effectively communicated complex technical information in previous roles. This could be through presentations, documentation, or team discussions.
How to prepare for a job interview at LevelUP HCS
✨Showcase Your Quantitative Skills
Be prepared to discuss your experience with market risk models and methodologies in detail. Highlight specific projects where you've applied time series analysis, VaR methodologies, or backtesting techniques, as this will demonstrate your expertise in the field.
✨Demonstrate Programming Proficiency
Since strong programming skills in SQL and Python are crucial for this role, be ready to talk about your experience with data handling and analysis. You might even want to prepare examples of how you've wrangled large datasets or implemented statistical tests in past roles.
✨Communicate Effectively
Excellent communication and presentation skills are essential. Practice explaining complex concepts in a clear and concise manner, as you may need to engage with various stakeholders, including front office and technology teams.
✨Prepare for Technical Documentation Questions
Given the importance of well-structured technical model documentation, be ready to discuss your approach to documenting model implementation details and findings. Consider bringing samples of your previous documentation to showcase your writing skills.
