eFX Quantitative Developer

UBS
London
2 months ago
Applications closed

Job Reference #
329083BR

Job Type
Full Time

Your role

  • We are seeking an experienced candidate to join our eFX Quantitative Developer team within UBS Global Markets
  • This is a fast paced and collaborative team that are responsible for the development and enhancement of the best-in-class eFX platform
  • The team sits within the Global Markets Principal Flow Trading stream, and has a business reporting line
  • You will be operating within a high-performing, fast paced quant development team, whose goals are directly aligned to the success of the business
  • You will take ownership of initiatives from initial analysis through to design, implementation and delivery
  • You will proactively suggest and drive through improvements to the platform and our framework
  • You will be involved in every aspect of algorithmic trading:
  • Market connectivity
  • Designing, implementing and back-testing pricing and execution strategies
  • Designing and building analytics to assess model and platform performance
  • Latency analysis and optimisation
  • Enhancing the proprietary eTrading framework that is used across the department

Your team

  • A highly technical and innovative quant development team leading automated trading in FX
  • Part of the Principal Flow Trading Quant Development department, alongside teams aligned to eRates, eCredit, FX Derivatives, Equity Derivatives, and Cash Equities
  • Focused on maximising automation and performance in order to drive eTrading revenues
  • Operating in a highly agile manner, releasing to production multiple times per day
  • The team is known for being collaborative and diverse, with a mandate to deliver meaningful change

Your expertise

  • Strong business knowledge of electronic trading, ideally eFX
  • Proven experience in designing and implementing low-latency, high-throughput, event-driven algorithmic trading platforms
  • Collaboration with quantitative analysts to design and implement algorithmic trading models and controls
  • Experience of producing model documentation and partnering with governance and second line of defence functions
  • Advanced Java programming skills including approaches to low-latency Java like lock free data structures and low-garbage programming techniques
  • Beneficial to have low level experience with messaging libraries and protocols including Aeron, Kafka, EMS, SBE, FIX, ITCH, OUCH
  • Familiarity with time-series databases (preferably KDB) and Python for building analytics and reports
  • Full stack development experience is an advantage (preferably React) particularly for building monitoring dashboards and trader-facing tools

About Us
UBS is the world’s largest and the only truly global wealth manager. We operate through four business divisions: Global Wealth Management, Personal & Corporate Banking, Asset Management and the Investment Bank. Our global reach and the breadth of our expertise set us apart from our competitors.

We have a presence in all major financial centers in more than 50 countries.

Join us
At UBS, we know that it's our people, with their diverse skills, experiences and backgrounds, who drive our ongoing success. We’re dedicated to our craft and passionate about putting our people first, with new challenges, a supportive team, opportunities to grow and flexible working options when possible. Our inclusive culture brings out the best in our employees, wherever they are on their career journey. And we use artificial intelligence (AI) to work smarter and more efficiently. We also recognize that great work is never done alone. That’s why collaboration is at the heart of everything we do. Because together, we’re more than ourselves.

We’re committed to disability inclusion and if you need reasonable accommodation/adjustments throughout our recruitment process, you can always contact us.

Disclaimer / Policy Statements
UBS is an Equal Opportunity Employer. We respect and seek to empower each individual and support the diverse cultures, perspectives, skills and experiences within our workforce.

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.