2026 | EMEA | London | FICC and Equities (Sales and Trading) Quantitative Strats | Seasonal/Off Cycle Internship

eFinancialCareers
Greater London
6 months ago
Applications closed

Related Jobs

View all jobs

Business Data Analytics Apprentice | Prentis Dadansoddeg Data Busnes - Pontypridd, Wales

Graduate Data Analyst

Data Engineer

Intern - Business Intelligence & Performance Reporting - (Fixed Term) - GLA14952

Masterdata Analyst

Data Science PhD internship (Operations Research)

2026 | EMEA | London | FICC and Equities (Sales and Trading) Quantitative Strats | Seasonal/Off Cycle InternshipAbout Us

About the program

Our Off-Cycle Program varies in length based on program/university for undergraduate students. You will be fully immersed in our day-to-day activities.

As a participant, you will:

Receive training designed to help you succeed Have the opportunity to work on real responsibilities alongside fellow interns and our people

Submitting Your Application

Each applicant has the opportunity to apply to up to 4 separate business / locationbinations in any given recruiting year. Any additional application will be auto withdrawn. In order to apply to an additional opportunity, you must withdraw a current application that has not been turned down. A single applicant should not create multiple email addresses to apply to additional opportunities
About the Team

About the division

Global Banking & Markets (Public) / FICC and Equities (Sales and Trading) enables our clients to buy and sell financial products, raise funding and manage risk. We make markets and facilitate client transactions in fixed ie, equity, currency andmodity products.

We make markets in and clear client transactions on major stock, options and futures exchanges worldwide. Through our global sales force, we maintain relationships with our clients, receiving orders and distributing investment research, trading ideas, market information and analysis.

Our quantitative strategists are at the cutting edge of our business, solving real-world problems through a variety of analytical methods. As a member of our team, you will use your advanced training in mathematics, programming and logical thinking to construct quantitative models that drive our success in global financial markets. Your talents for research, analysis and aptitude for innovation will define your contributions and enable you to find solutions to a broad range of problems, in a dynamic, fast-paced environment. Whatever your background, you will bring a fresh perspective and unique skillset to our business. In return, you will be trained by our experts across the firm to navigate theplexities of the financial markets and state-of-the-art methods in quantitative finance. Job ID 300

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.