Quantitative Developer

GSR
City of London
2 months ago
Applications closed

Related Jobs

View all jobs

Quantitative Developer

Quantitative Developer

Quantitative Developer

Senior Quantitative Developer

Senior Quantitative Developer

Senior Quantitative Developer

Quantitative Developer

Location: London, United Kingdom


About GSR: Founded in 2013, GSR is a leading market‑making and programmatic trading company in the fast‑evolving world of cryptocurrency trading. With more than 200 employees in 5 countries, we provide billions of dollars of liquidity to cryptocurrency protocols and exchanges daily. We build long‑term relationships with cryptocurrency communities and traditional investors by offering exceptional service, expertise, and trading capabilities tailored to their specific needs.


About The Role

Deliver high‑performance trading systems in Rust that directly drive strategy execution and profitability. Enable traders and researchers to operate at scale by building robust infrastructure, analytics tools, and automation. Shape the architecture behind live trading, reduce latency, and solve complex real‑time challenges in collaboration with a high‑calibre, cross‑functional team.


Responsibilities

  • Design, develop, and maintain a low‑latency trading system in Rust.
  • Collaborate with traders, engineers, and quants to design and implement trading strategies within market‑making, prop, and OTC.
  • Build new tools/infrastructure to facilitate research; e.g., analytics and optimization.
  • Automate the deployment and monitoring of trading strategies.
  • Troubleshoot and resolve technical issues in real‑time.

Your Profile

  • Minimum of one year experience developing in Rust; will be tested.
  • Familiarity with core trading strategies (e.g., market‑making, arbitrage, execution).
  • Strong understanding of algorithms and data structures, as well as quant finance concepts: limit‑order books, market microstructure, pricing.
  • Experience with real‑time data processing, IPC/shared‑memory architectures, and low‑allocation/zero‑copy design.
  • A Bachelor's degree (minimum) or PhD (preferred) in Computer Science, Mathematics, Physics, or a related field.
  • Prior experience in high‑frequency trading, market‑making, or other electronic trading environments is a strong advantage but not required.

What We Offer

  • A collaborative and transparent company culture founded on Integrity, Innovation, and Performance.
  • Competitive Salary with two discretionary bonus payments a year.
  • Benefits such as Healthcare, Dental, Vision, Retirement Planning, 30 days holiday, and free lunches when in the office (benefits vary depending on employment location).
  • Regular Town Halls, team lunches, and drinks.
  • A Corporate and Social Responsibility program as well as charity fundraising matching and volunteer days.

Equal Employment Opportunity

GSR is proudly an Equal Employment Opportunity employer. We do not discriminate based on any applicable legally protected characteristics such as race, religion, color, country of origin, sexual orientation, gender, gender identity, gender expression, or age. We operate a meritocracy; all aspects of people engagement from the decision to hire or promote as well as our performance management process will be based on the business needs and individual merit, competence in the role.


#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.