Quantitative Analyst

City of London
3 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 (Equities & Equity Derivatives - VP)

Job Title: Quantitative Analyst

Location: London

Contract Length: 6 Months (Jan 26 start date)

Day rate: £850 - £950 via Umbrella

ROLE DESCRIPTION

The role will require development of the underlying mathematical models and analytical tools used by the FX, Fixed Income, Credit, or Equities desks.
To design, develop, test and document the models developed to standards
Develop technical solutions for the desk as required
To provide rapid fixes to any issues identified in the models
To develop model calibration routines and market data analytics (such as curve bootstrapping and interpolation)

Certifications, Qualifications and Experience (Minimum requirements of the Job)

1-5 years working as a Quantitative Analyst developing models in quantitative finance, IT development, or a trading environment
A degree in mathematical finance, science or maths from a top tier university
Knowledge of the standard pricing models used in the investment banking industry
C++ or C experience
Excel VBA preferred
Python experience preferred

Knowledge, Skills & Experience

Solid background in stochastic processes, probability and numerical analysis. Physics, Engineering or similar subjects is desirable, but not strictly required.
Knowledge of main instruments used in FX, Fixed Income, Credit, or Equities
Knowledge of CVA, CSA discounting, VaR, ES and other risk measures.
Strong C++ or C skills.
Knowledge of at least one of the following scripting languages: Python, Perl, Shell Script, C#, Java, VBA.
Good knowledge of Excel.
Knowledge of Windows and UNIX/LINUX, understanding of and experience with version control systems (GIT) and distributed development process.
Knowledge of distributed computing and serialisation techniques preferred.
Ability to work in fast-paced environment with proven ability to handle multiple outputs at one time

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.

Careers

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.

Jobs

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.

Education

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.