Quantitative Analyst - Director (Equity Derivatives)

Huxley Associates
City of London
2 months ago
Applications closed

Related Jobs

View all jobs

Quantitative Analyst (Equities & Equity Derivatives - VP)

Quantitative Analyst

Quantitative Analyst

Quantitative Analyst

Quantitative Analyst

Quantitative Analyst

Director - Quantitative Analyst (Equity Derivatives)

Location: London
Division: Front Office - Investment Banking


About the Role: We are looking for an experienced Quantitative Analyst (Director level) to join a Investment Banking client within their Front Office - Equity Derivatives team. This is a high-impact role focused on developing and maintaining advanced pricing models for complex equity derivative products.


Key Responsibilities:



  • Lead the design, development, and enhancement of pricing models for equity derivatives, including exotic structures.
  • Implement robust calibration frameworks for volatility surfaces and correlation models.
  • Partner with traders and structurers to deliver accurate pricing and risk analytics for products such as autocallables, cliquets, and other structured payoffs.
  • Ensure model compliance with regulatory standards and internal governance.
  • Drive innovation in modeling techniques and integration into production systems.

Required Skills & Experience:



  • Advanced degree (Master's or PhD) in Mathematics, Physics, Engineering, or Quantitative Finance.
  • Extensive experience in equity derivatives pricing, ideally within an investment banking front-office environment.
  • Strong knowledge of exotic equity derivatives and structured products.
  • Expertise in model calibration, stochastic modeling, and numerical methods.
  • Proficiency in programming languages such as Python, C++, or similar.
  • Excellent communication and leadership skills.

Preferred:



  • Experience with Monte Carlo simulations, PDE solvers, and optimisation techniques.
  • Familiarity with regulatory frameworks (e.g., FRTB, XVA).

Apply now to avoid disappointment.


To find out more about Huxley, please visit (url removed)


Huxley, a trading division of SThree Partnership LLP is acting as an Employment Business in relation to this vacancy Registered office 8 Bishopsgate, London, EC2N 4BQ, United Kingdom Partnership Number OC(phone number removed) England and Wales


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

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

Industry Insights

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

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.