Mondrian Alpha | Python Developer - Software Developer - London - Hedge Fund - Systematic Strategy - Market-Leading Compensation

Mondrian Alpha
London
1 year ago
Applications closed

Our client, a prestigious London-based Hedge Fund, with over £5 billion in assets under management (AUM) seeks a senior Python Developer to join their core trading desk technology team. We are looking for Software Engineers with strong Python experience to join this fund with a focus on systematic trading strategies.


Ready to make your application Please do read through the description at least once before clicking on Apply.

This is a unique opportunity for a Python Developer to be part of a dynamic, cutting-edge environment that offers unrivaled progression, autonomy, and the chance to unleash your creativity.

Responsibilities:

  • Collaborate with quantitative researchers and traders to develop and maintain robust and scalable Python-based trading systems and tools.
  • Design, implement, and optimize code to support high-frequency trading strategies, market data analysis, and risk management.
  • Contribute to the full software development lifecycle, including requirements gathering, testing, deployment, and post-implementation support.
  • Identify areas for improvement and propose innovative solutions to enhance trading efficiency and performance.
  • Work closely with cross-functional teams to integrate new technologies and methodologies into existing infrastructure.

Requirements:

  • Extensive experience as a Python Developer, ideally within the finance industry or a similarly demanding environment.
  • Strong proficiency in Python and related libraries/frameworks (NumPy, pandas, SciPy, etc.).
  • Solid understanding of algorithmic trading principles, market microstructure, and risk management concepts.
  • Previous exposure to systematic trading strategies, quantitative research, or high-frequency trading is highly desirable.
  • Proven ability to write clean, efficient, and well-documented code.
  • Experience with large datasets, real-time data processing, and distributed systems is advantageous.
  • Excellent problem-solving and analytical skills with a keen attention to detail.
  • Strong communication skills to collaborate effectively with both technical and non-technical stakeholders.

What they offer:

  • Competitive compensation package ranging between £250,000 and £300,000, commensurate with experience.
  • Unparalleled career progression opportunities within a leading hedge fund.
  • Autonomy and creative freedom to innovate and contribute to the firm's success.
  • State-of-the-art offices with exceptional amenities, including an onsite gym and a canteen providing complimentary breakfast, lunch, and dinner.
  • Full medical coverage and additional benefits.

Apply now with your latest CV and a cover letter highlighting your relevant experience and achievements.

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.