Quantitative Finance

Quantitative finance has gained popularity and a higher profile in recent years for a number of reasons including high salaries, increased market share and the continuous rise in the use of AI and machine learning tools.

It’s important to look beyond the headlines and understand that it encompasses a diverse skill set, spanning different organisations and roles which require different qualifications.

The majority of quantitative finance roles require a STEM (Science, Technology, Engineering & Maths) degree, depending on the role, this can be at undergraduate, Master’s, PhD/DPhil level. Most roles require strong programming and mathematical skills that have been developed during your studies, extra-curricular activities, projects and work experience.

E-Financial Careers’ annual Student Guide to Careers in Banking & Financial Markets has a useful introduction to the various aspects of the sector. We also have a profile of an Oxford alumnus working as a Trader at Optiver in the 2026 edition of the Oxford Guide to Careers.

Types of job

Job titles are varied and not always consistent across different firms, often meaning something completely different, with different levels of responsibility and compensation. Most firms have detailed job descriptions for their various roles, read these to gain a good understanding of what the role is and their expectations of applicants.

N.B. Focus on the specifics of the role and job description not the title. Some of the more familiar job titles are listed below:

• Quantitative Trader 
• Quantitative Researcher
• Quantitative Strategist
• Software Engineer
• Risk Manager

Types of Firms

As mentioned above, there are many types of firms that hire “quant specialists” or “quants”, below is a list of some of the more popular ones. Try to look beyond the most high profile names and research the range of firms that you can work at. Be aware that competition is fierce, at some of the most popular firms applicant success rates can be less than 1%. This shouldn’t put you off, but emphasises that importance of doing your research, and identifying which firms and roles best fit your skillset and qualifications.

Investment Banks

These are often highly diversified financial institutions eg: HSBC, Goldman Sachs, UBS etc. offering a range of services to clients. Quantitative specialists may work with different teams across the bank.

Proprietary Trading Firms

These are often the most visible firms in the sector eg: Jane Street, Susquehanna etc. and are sometimes known as "electronic market-makers".

Hedge Funds

These are asset management firms investing money for their clients, institutional investors (eg: pension funds) and high net worth individuals eg: Citadel, Two Sigma, D. E. Shaw.

Private Equity

Private Equity (PE) firms earn money by charging management and performance fees from investors in a PE fund. For example a PE fund may acquire a private company, improve its performance by restructuring and selling it. The larger firms in this area include: Blackstone, The Carlyle Group and KKR.

Insurance/Re-insurance

These are firms eg: in which mathematical and risk modelling are key to their day to day work and this can span a range of insurable assets and factors they are insured against.

Tech firms

These firms eg: Apple, Alphabet, Microsoft, Nvidia, hire “quants” to work on their machine learning and AI systems.

Skills

Academic background - Most professionals hold a STEM degree, commonly in mathematics, statistics, computer science, physics or engineering. Some firms value PhD-level expertise, while others prefer to train strong undergraduates and masters students. Specialist Master’s programmes in financial engineering or quantitative finance can be valuable if they are from a well-regarded institution, with a strong placement record.

Additional, specific skills and levels of expertise will vary for each role (read job descriptions carefully). Below is a list of some key skills:

• Mathematical foundations - Fluency in probability, statistics, linear algebra, optimisation and stochastic processes is essential. These are applied in everything from risk models to trading signals.
• Programming that scales - Most firms expect you to code confidently. Python is widely used for research, data work and machine learning. C++ remains standard in high-performance systems. Java, and sometimes have their own programming languages which you will learn once you join. Employers don’t expect you to know them all, but you should be able to show depth in at least one and transferable skills across others.
• Data and machine learning literacy - Handling large, messy datasets is routine. You should be comfortable with SQL, data structures, and tools like NumPy or Pandas. Many interviews now include machine learning topics such as regression, classification and model validation.
• Problem solving under pressure - Interviews often include puzzles, probability questions or estimation problems designed to test your reasoning. Practicing clear, structured thinking is as important as reaching the correct answer.
• Accuracy and testing - A small coding or modelling error can have large financial consequences. Firms value habits like unit testing, version control and clear documentation.
• Other core skills - Curiosity, creativity, humility and integrity are repeatedly emphasised by employers. “Quants” often work across a range of teams such as research, trading, engineering and risk, so clear communication and collaboration are vital.
• Finance and domain knowledge - Understanding how markets function helps you build realistic models and interpret results. You don’t need to be an expert from day one, but you should be able to link your technical work to trading and investment decisions.

Getting experience

There are range of ways to gain relevant experience and we have highlighted some below:

• Projects with results - Reproducing academic papers, building back-tests, running event studies or optimising execution algorithms all give you material to discuss in applications and interviews. Sharing code or short write-ups can strengthen your profile.
• Competitions - Coding, modelling and data challenges (such as Kaggle or trading competitions hosted by the firms themselves), Maths Olympiads (and their equivalents), allow you to practise under pressure and benchmark yourself against peers.
• Internships and insight days - These offer the clearest view of the work and help you gain exposure to different firms and roles. They are also often a pipeline for graduate recruitment.  In addition to formal internships, try to find other ways to get experience in and exposure to the sector. This can be done by creating work experience opportunities for yourself (eg: contacting smaller firms to ask if they can offer you some short work experience) and/or applying to any relevant internships via our Micro and Summer Internship programmes.
• Join a society and get practice - Many firms test mental maths, probability and coding in timed conditions. Building regular practice into your preparation helps you stay calm and structured on the day. Joining a society to practise this, alongside any coding can be extremely helpful.

Entry points and getting a job

There are many different entry points. As mentioned above, many firms offer Insight Days/weeks, internships and full-time graduate roles. There is no cohesive recruitment timeline. Some firms have open positions (internships & graduate roles) throughout the year and some only open for specific periods. It’s important for you to find out and keep up to date with individual firms’ processes and timelines. Some firms also recruit from the UK for their international offices, therefore in addition to London, you may be eligible for roles in other locations.

If you can, it’s ideal to attend an Insight Day/week or take part in an internship which will give you practical exposure to what the role entails.

This is a very competitive and popular career choice for STEM graduates. Top ranked students apply from the leading academic institutions across the world, allowing employers to choose from a very wide range of highly qualified applicants. It’s important to do in-depth research to discover what each role entails and whether you have the requisite skill set. Our advice is to apply as widely as possible to different types of firms (always taking the time to write good applications).

Many firms host events at Oxford University and in the past have attended our Careers Fairs, hosted individual presentations, partnered with student societies and departments, therefore, there should be plenty of opportunities to engage with them to learn more.

Resources

• See our briefings on Banking & Investment and Tech: IT, Data, Machine Learning and AI
• E-Financial Careers Guide to Banking & Financial Markets. The website www.efinancialcareers.com also has archived articles on quantitative career paths and options
• Financial Times – information about global financial markets (free to current Oxford students/staff via SOLO)
• Employer websites.

Equality, Diversity & Inclusion

Some firms offer programmes dedicated to under-represented groups in the industry eg: exclusive Insight Days and programmes for women. 

The UK Equality Act 2010 has a number of protected characteristics to prevent discrimination due to your age, disability, gender reassignment, race, religion or beliefs, sex or sexual orientation. For further information, visit the Equality and Human Rights Commission’s webpage on the Equality Act and the UK Government’s webpages on discrimination.