Market Risk Quantitative Analytics Consultant (Contract)

LevelUP HCS
London
1 week ago
Create job alert

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 programing 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.

Related Jobs

View all jobs

Senior LNG Analyst

Senior Buyer

Private Equity Real Estate Analyst

Backend Engineering Lead

Equity Quant Developer- Temp

Fluent Mandarin Treasury Sales Trader

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Tips for Staying Inspired: How Data Science Pros Fuel Creativity and Innovation

Data science sits at the dynamic intersection of statistics, computer science, and domain expertise, driving powerful innovations in industries ranging from healthcare to finance, and from retail to robotics. Yet, the daily reality for many data scientists can be a far cry from starry-eyed talk of AI and machine learning transformations. Instead, it often involves endless data wrangling, model tuning, and scrutiny over metrics. Maintaining a sense of creativity in this environment can be an uphill battle. So, how do successful data scientists continue to dream big and innovate, even when dealing with the nitty-gritty of data pipelines, debugging code, or explaining results to stakeholders? Below, we outline ten practical strategies to help data analysts, machine learning engineers, and research scientists stay inspired and push their ideas further. Whether you’re just starting out or looking to reinvigorate a long-standing career, these pointers can help you find fresh sparks of motivation.

Top 10 Data Science Career Myths Debunked: Key Facts for Aspiring Professionals

Data science has become one of the most sought-after fields in the tech world, promising attractive salaries, cutting-edge projects, and the opportunity to shape decision-making in virtually every industry. From e-commerce recommendation engines to AI-powered medical diagnostics, data scientists are the force behind innovations that drive productivity and improve people’s lives. Yet, despite the demand and glamour often associated with this discipline, data science is also shrouded in misconceptions. Some believe you need a PhD in mathematics or statistics; others assume data science is exclusively about machine learning or coding. At DataScience-Jobs.co.uk, we’ve encountered a wide array of myths that can discourage talented individuals or mislead those exploring a data science career. This article aims to bust the top 10 data science career myths—providing clarity on what data scientists actually do and illuminating the true diversity and inclusiveness of this exciting field. Whether you’re a recent graduate, a professional looking to pivot, or simply curious about data science, read on to discover the reality behind the myths.

Global vs. Local: Comparing the UK Data Science Job Market to International Landscapes

How to evaluate salaries, opportunities, and work culture in data science across the UK, the US, Europe, and Asia Data science has proven to be more than a passing trend; it is now a foundational pillar of modern decision-making in virtually every industry—from healthcare and finance to retail and entertainment. As the volume of data grows exponentially, organisations urgently need professionals who can transform raw information into actionable insights. This high demand has sparked a wave of new opportunities for data scientists worldwide. In this article, we’ll compare the UK data science job market to those in the United States, Europe, and Asia. We’ll explore hiring trends, salary benchmarks, and cultural nuances to help you decide whether to focus your career locally or consider opportunities overseas or in fully remote roles. Whether you’re a fresh graduate looking for your first data science position, an experienced data professional pivoting from analytics, or a software engineer eager to break into machine learning, understanding the global data science landscape can be a game-changer. By the end of this overview, you’ll be better equipped to navigate the expanding world of data science—knowing which skills and certifications matter most, how salaries differ between regions, and what to expect from distinct work cultures. Let’s dive in.