Senior Quantitative Analyst

Capstone Investment Advisors
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Quantitative Data Scientist, Energy Modelling, Hybrid

Senior Insurance Pricing Analyst — Data Science & Modelling

Senior Analyst – Data Science

Funds Technology – Data Analyst Manager Assistant Manager Senior Consultant

Senior Risk Modelling Data Scientist

Senior Data Scientist - Game Analytics

RESPONSIBILITIES:

Experience with exotic equity products and scripted payoffs (ex. BLAN). Working knowledge of different volatility models (Local Volatility, Local Stochastic Volatility, Parametric Implied Volatility). Understanding of Monte-Carlo simulations. Ability to communicate efficiently and concisely in writing and verbally. Programming skills in at least one language, preferably C++. SKILLS AND QUALIFICATIONS: Bachelors, Masters, or PhD in STEM or similar subject. Direct experience working with equities. Strong analytical and problem-solving skills. Strong quantitative skills and experience in statistical analysis. Collaborative team player with strong verbal communication skills. 8+ years working experience in the financial industry (buy or sell side). BONUS SKILLS AND QUALIFICATIONS: Programming skills in Java andor Python. Capstone is committed to creating an inclusive environment where we welcome people of different backgrounds. Capstone considers applications for employment without regard to all applicable protected characteristics, including race, color, religion, ethnicity, national origin, gender, sexual orientation, gender identity or expression, age, parental status, veteran status, or disability status.

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.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

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.