Quantitative Developer

Pharo Management
City of London
4 months ago
Applications closed

Related Jobs

View all jobs

Quantitative Developer

Quantitative developer

Senior Quantitative Developer

Senior Quantitative Developer

Senior Quantitative Developer

Quantitative Analyst

Pharo Management is a leading global macro hedge fund with a focus on emerging markets. Founded in 2000, the firm has offices in London, New York and Hong Kong and currently manages $7 billion in assets across four funds. Pharo trades foreign exchange, sovereign and corporate credit, local market interest rates, commodities, and their derivatives. We trade in over 70 countries across Asia, Central and Eastern Europe, the Middle East and Africa, Latin America as well as developed markets. Our investment approach combines macroeconomic fundamental research and quantitative analysis.

Pharo employs a diverse, dynamic team of 130 professionals representing over 20 nationalities and 30 languages. We have a strong corporate culture anchored in core values such as collaborative spirit, creativity, and respect. We are passionate about what we do and are committed to attracting the best and brightest talent.

This is a great opportunity to join a market leader, and contribute to our continued success.

Responsibilities
  • Implement, test, and maintain pricing models and risk infrastructure
  • Write production-grade Python code with a strong emphasis on readability, performance, and testing
  • Collaborate in code reviews, pair programming, and team design discussions
  • Enhance CI/CD pipelines, testing frameworks, and logging/monitoring systems
  • Work on fixed income and derivatives products, including IR, FX, bonds, and options
  • Partner with quants to understand requirements and translate them into robust engineering solutions
Skills & Experience

Required

  • Degree in Computer Science, Engineering, Mathematics, Physics, or related field
  • 0–5 years’ experience, or a strong graduate with demonstrable programming
  • Proficiency in Python (experience with pandas and numerical libraries)
  • Understanding of software engineering best practices (testing, CI/CD, version control with Git)
  • Strong problem-solving skills and ability to work with complex systems
  • Excellent communication and collaboration skills across teams and time zones
  • Willingness and enthusiasm to learn financial products and derivatives

Preferred

  • Some exposure to finance or financial instruments (fixed income, derivatives, options)
  • Experience working in a collaborative environment with code reviews and pair programming
  • Exposure to Git workflows and collaborative development practices
  • Familiarity with C++ (legacy codebase context)
  • Awareness of large-scale pricing libraries (QuantLib, Strata, or similar)


#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.

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.

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.

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.