Quant Developer

Experis
London
1 year ago
Applications closed

Related Jobs

View all jobs

AWS Data Engineer - AVP Capital Markets

AWS Data Engineer - VP Capital Markets

AWS Data Engineer - VP Capital Markets

AWS Data Engineer - AVP Capital Markets

AI/MLOps Platform Engineer

AI/MLOps Platform Engineer

Location: London Job Type: Contract Industry: Cloud & Infrastructure Job reference: BBBH384924_1731406913 Posted: about 2 hours ago

Job Title: Quantitative Developer
Duration: Six Months (with potential for extension)
Location: London (Hybrid Work Model)

Role Overview

Our Equity Derivatives Quant team within Global Banking and Markets is seeking a skilled C++/Python Quant Developer with a strong background in Structured Equity Derivatives. This role will focus on enhancing and maintaining our pricing, risk, and P&L infrastructure to support a high-performance trading platform.

Key Responsibilities

Pricing and Risk Infrastructure: Collaborate in designing and implementing infrastructure for pricing, risk management, and P&L functionalities that support the core pricing library. Quantitative Library Development: Work alongside Quantitative Modellers to evolve and optimize the core pricing library. Tooling and Platform Support: Build and maintain quantitative tools necessary for supporting the platform's operational and analytical needs.

Project Focus Areas

FRTB IMA Regulatory Reporting: Develop calculation infrastructure to meet Fundamental Review of the Trading Book (FRTB) internal model approach (IMA) regulatory standards. Risk and P&L Calculations: Design and implement end-of-day and intraday risk/P&L calculations, enabling the phase-out of legacy platforms. Market Data Pipelines: Create automated data marking pipelines for market data processing and integration.

Collaboration and Interaction

The successful candidate will engage closely with trading desks, other quantitative analysts, Risk and Finance teams, and broader technology teams. While based in London, the role involves coordination with teams and clients across London, Paris, Hong Kong, and Bangalore, and may require occasional travel.

Requirements

Essential Qualifications and Skills

Experience: 3-7 years in a quantitative finance, IT development, or trading environment, ideally as a Quantitative Analyst. Educational Background: Bachelor's or Master's degree in mathematical finance, mathematics, science, or related field from a top-tier university. Technical Proficiency: C++: Minimum 2 years (experience with Visual Studio 2017 preferred)Python: Minimum 2 years Domain Knowledge: Understanding of standard pricing models used in the investment banking sector.

Preferred Skills and Knowledge

Quantitative Expertise: Knowledge in stochastic processes, probability, and numerical analysis; backgrounds in physics, engineering, or similar disciplines are advantageous. Data and Instrument Knowledge: Experience with data analysis and familiarity with primary equity and equity derivatives instruments.Knowledge of instrument pricing, sensitivity analysis, P&L prediction and explanation, and risk measures like VaR and Expected Shortfall (ES). Technical Skill Set: Experience with distributed computing and data serialization. Proficiency in Excel and experience with CI/CD pipeline tools. Soft Skills: Ability to thrive in a fast-paced environment and manage multiple priorities efficiently.

This role offers the opportunity to contribute to a globally integrated team, engage in impactful projects, and support cutting-edge trading and risk management systems.

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 to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.