Software Developer (Research Infrastructure) - £350/500,000 - Quantitative Trading

Campbell North Ltd.

City of London

1 month ago

Create job alert

Software Developer (Research Infrastructure) | Quantitative Trading | £350–500k

How many opportunities will you get to develop a greenfield platform that directly shapes the future of a multi-billion-dollar prop trading business? I'd imagine this is one of few.

Here, you'll develop a proprietary Python-based research platform from scratch for one of the leading Quantitative Traders globally.

The challenge isn’t just performance or raw throughput - though both matter. It’s building a flexible, robust system capable of supporting a wide range of research workflows, each with its own quirks, data dependencies, and computational demands.

You won't just be writing clean, efficient Python, you'll be working directly with researchers to understand their methodology, tooling, and pain points. The system you help design will either accelerate the productivity of some of the smartest minds in quantitative finance - or get in their way.

There’s no single background that guarantees success here, but mastery of Python, a deep understanding of system design, and a collaborative mindset are non-negotiable. While most of the team comes from big-tech, there's space for exceptional buy-siders too.

Here, culture matters. This is a flat, collaborative team with no space for ego. They move fast, build carefully, and share success (and failures) together. If you're looking for a heads-down, siloed role, this isn't the right fit.

If you want to be involved from the beginning - shaping how this platform evolves from scratch - now is the time to reach out.

What they’re looking for:

2–10 years of experience in a high-performance, engineering-driven environment
Advanced Python proficiency
Experience designing and building complex, data-intensive systems
Strong Linux fundamentals
Excellent communication skills and a comfort working closely with non-engineering stakeholders
Great to have: experience with containerization (e.g., Docker, K8,) and exposure to Golang

If this sounds interesting - or if you're simply curious to learn more - reach out for a conversation.

The team is actively hiring and will move quickly for the right person.

#J-18808-Ljbffr

Related Jobs

View all jobs

Software Developer - Data Analytics Team

Graduate Software Developer / Quantitative Developer / Quantitative Researcher - Up to £160,000[...]

Quantitative Software Developer

Junior Data Analytics & Visualization Engineer (Hybrid)

Quantitative Developer, Fixed Income

Data Scientist

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Feb 10, 2026

Careers

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.

Feb 3, 2026

Jobs

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.

Jan 23, 2026

Education

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.