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If you're a recent graduate or soon-to-be graduate join us and unlock a world of carefully curated experiences, knowledge and connections to shape your career.
Along the way, you can expect all the training and support you need to make your mark on the world. To put it simply, we’ll empower you to help determine how we do things and where we go next.
Our graduates are a vital part of our success and we welcome applications from people from all walks of life. Whoever you are and wherever you want to join us, if you’re curious, creative and ambitious, this is a world in which you can truly belong.
Why the Quantitative Analytics Graduate Programme?
Our industry-leading group provides model development, analytics and valuable quantitative advice to businesses across the bank.
Applying to our programme means the opportunity to join one of our Statistical Modelling teams. Comprised of data scientists, developers, data engineers and researchers, the teams directly support the Finance, Treasury, Fraud Surveillance, Stress Testing, Climate Risk, as well as the Wholesale and Retail Credit Risk operations within the bank.
Team members deliver solutions to develop, test, implement and support all statistical and econometrics models for the estimation of default probabilities, recovery rates, exposures at default, forecasting models for net revenue, balance sheet projections, scenario generation/expansion, operational risk, climate change, economic capital models and machine learning models for fraud detection, all while using the latest model development approaches and advancements in technology.
As a Quantitative Analytics Graduate, your experience at Barclays will begin with several weeks of intense training, covering product and business knowledge as well as other skills you’ll need for a successful start.
After training, you’ll join a specific desk and collaborate with colleagues on active projects, giving you ample opportunities to grow and learn.
During the programme you’ll
- Participate in formal and informal training designed to give you the knowledge you need when you need it
- Receive detailed performance coaching and feedback
- Have opportunities to expand your network and develop leadership skills
1-2 year targeted, fast-track programme focused on developing specialist technical expertise.
Quantitative Analytics at Barclays
Barclays Quantitative Analytics team is a global organisation of highly specialised modellers and developers responsible for researching, innovating, developing, testing, implementing and supporting all quantitative models used for valuation and risk management across all asset classes.
Who we're looking for
To be considered for this programme, you must be motivated, curious and have completed or be in your final year of an undergraduate or postgraduate qualification, or equivalent, in a technical discipline such as Physics, Mathematics, Quantitative Finance, Economics, Statistics, Calculus, Computer Science, or other STEM subjects.
Ideally, you'll have mathematical and programming skills (ideally in Java, C++, or Python) along with a knack for logical thinking and creative problem solving. You'll be a good communicator and team player.
Working in Glasgow
Barclays Glasgow campus is a world-class, unconventional working environment with incredible facilities in an easy to reach central location.
There is a huge variety of working, meeting and thinking spaces and a warm, friendly and open feel across the campus. Its a place where people enjoy spending time. A place for eating, socialising and entertaining, as well as working.
A place with a buzz about it where people will look forward to being there for two or more days a week.
It is the policy of Barclays to ensure equal employment opportunity without discrimination or harassment on the basis of race, colour, creed, religion, national origin, alienage or citizenship status, age, sex, sexual orientation, gender identity or expression, marital or domestic/civil partnership status, disability, protected veteran status, genetic information, or any other basis protected by law.