Crypto | Quantitative Researcher

Qube Research & Technologies Limited
London
3 weeks ago
Applications closed

Related Jobs

View all jobs

Crypto Quantitative Developer

Crypto Quantitative Developer

Head of Data Analytics

Head of Data Analytics

Remote Quantitative Researcher for Crypto HFT

Quantitative developer

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology and trading expertise has shaped QRT’s collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors.


Your future role within QRT:

  1. Create high quality Crypto signals on various exchanges and platforms.
  2. Monitor your own trading, strategy performance and all relevant risks.
  3. Your core objective is to create high quality predictive signals.
  4. By leveraging access to large and diversified datasets you will identify statistical patterns and opportunities.
  5. Share and discuss research results, methodology, data sets and processes with other researchers.
  6. Implement the signals and the relevant datasets within the global execution platform.
  7. Monitor signal behaviour and model performance over time within your strategies.
  8. You would lead the full strategy research cycle from signal generation to implementation.
  9. Proven track record in delivering successful Crypto systematic strategies.
  10. Minimum 2 years of experience in the financial industry.
  11. Advanced degree in a quantitative field such as data science, statistics, mathematics, physics or engineering.
  12. Strong knowledge in statistics, machine learning, NLP or AI techniques is a plus.
  13. Capacity to multi-task in a fast-paced environment while keeping strong attention to detail.
  14. Good fundamental crypto knowledge is a plus.
  15. Experience with one or more of the below is a plus:
  16. Crypto specific data sets, DeFi, latency-sensitive, ML/AI or strategies in production.
  17. Coding skills required in at least one leading programming language, Python preferred, C++ is beneficial.
  18. Intellectual curiosity to explore new data sets, solve complex problems, drive innovative processes and connect the dots between multiple fields.
  19. Capacity to work with autonomy within a collegial and collaborative environment.
  20. Strong capacity to communicate with technologists, data scientists and traders across the globe.

QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees to achieve a healthy work-life balance.


#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.

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.

Data Science Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.