Quantitative Trader

CW Talent Solutions
London
2 months ago
Applications closed

Related Jobs

View all jobs

Quantitative Researcher/ Trader

Graduate Quantitative Analyst (Entry Level)

Quantitative Developer

Quantitative Analyst (Equities & Equity Derivatives - VP)

Senior Quantitative Developer

Quantitative Analyst (Equities & Equity Derivatives - VP)

Quantitative Trader – Relative Value Volatility

Location: Dubai


CW Talent Solutions is pleased to partner with a leading global trading firm to recruit an experienced Quantitative Trader for its growing London team. This role offers a unique opportunity to operate at the intersection of quantitative research, engineering, and automated trading, contributing directly to the development of high-performance trading strategies across global markets.


Key Responsibilities

  • Develop high-quality predictive signals for quantitative trading strategies
  • Identify statistical patterns and trading opportunities using large, diverse datasets
  • Collaborate with fellow researchers by sharing research findings, methodologies, datasets, and processes
  • Implement signals and datasets within the firm’s global execution platform
  • Monitor signal behavior and model performance over time
  • Lead the full research lifecycle, from signal generation through to deployment and execution


Preferred Skills & Experience

  • Proven experience as a Quantitative Trader, Quantitative Researcher, or in data science / machine learning
  • Advanced degree in a quantitative discipline such as Statistics, Mathematics, Physics, Engineering, or Data Science
  • Strong background in statistics, machine learning, NLP, or AI techniques
  • Ability to manage multiple priorities in a fast-paced environment with strong attention to detail
  • Proficiency in at least one major programming language (Python, R, MATLAB, C++, or C#)
  • Experience working with large datasets across multiple time horizons is advantageous
  • Strong intellectual curiosity and problem-solving mindset
  • Ability to work autonomously while contributing within a collaborative team environment
  • Excellent communication skills, with the ability to work effectively with technologists, data scientists, and traders globally
  • Demonstrated success in developing and delivering systematic trading strategies


Contact Gavin Mulhern

📞 +44 3300 522 127

📧

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.