eFX Quantitative Developer

UBS
City of London
4 months ago
Applications closed
Responsibilities

  • Operate within a high-performing, fast-paced quant development team whose goals are directly aligned to the success of the business.
  • Take ownership of initiatives from initial analysis through to design, implementation and delivery.
  • Proactively suggest and drive improvements to the eFX platform and its underlying framework.
  • Involve in every aspect of algorithmic trading: market connectivity, designing, implementing and back‑testing pricing and execution strategies, building analytics to assess model and platform performance, latency analysis and optimisation.
  • Enhance the proprietary eTrading framework used across the department.
  • Collaborate with quantitative analysts to design and implement algorithmic trading models and controls.

Qualifications

  • Strong business knowledge of electronic trading, ideally eFX.
  • Proven experience in designing and implementing low‑latency, high‑throughput, event‑driven algorithmic trading platforms.
  • Advanced Java programming skills, including low‑latency techniques such as lock‑free data structures and low‑garbage GC‑friendly design.
  • Beneficial 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, particularly React for monitoring dashboards and trader‑facing tools.
  • Experience producing model documentation and partnering with governance and second‑line defence functions.

About UBS

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 breadth of expertise set us apart from our competitors.


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


Equal Opportunity Employer

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.


#J-18808-Ljbffr

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.