Quantitative Developer, Systematic Equities

Millennium Management
London
2 months ago
Applications closed

Related Jobs

View all jobs

Regional Finance Director

Regional Finance Director

Finance Director

Quantitative Analyst

Quantitative Analyst - Sports Trading

Senior Quantitative Analyst

Quantitative Developer, Systematic Equities

Quantitative Developer, Systematic Equities

We are seeking a quantitative developer to partner focus on the development and subsequent optimization of infrastructure supporting the overall development and production of quantitative trading models. The ideal candidate will work directly with the quantitative researcher(s) and senior portfolio manager.

This team member will be responsible for the implementation of technology to enable large-scale computational efforts in quantitative research, as well as related efforts, such as the preparation and transformation of data and other operational tasks.

Preferred Location

London or Dubai preferred

Principal Responsibilities

  • Partner closely with the Portfolio Manager to develop data engineering and prediction tools primarily for the systematic trading of equities
  • Develop software engineering solutions for quantitative research and trading
    • Assist in designing, coding, and maintaining tools for the systematic trading infrastructure of the team
    • Build and maintain robust data pipelines and databases that ingest and transform large amounts of data
    • Develop processes that validate the integrity of the data
  • Implementation and operation of systems to enable quantitative research (i.e. large scale computation and serialization frameworks)
    • Live operation of such systems, including monitoring and pro-active detection of potential problems and intervention
  • Stay current on state-of-the-art technologies and tools including technical libraries, computing environments and academic research
  • Collaborate with the PM and the trading group in a transparent environment, engaging with the whole investment process

Preferred Technical Skills

  • Expert in Python and/or KDB/Q
  • Proficient in modern data science tools stacks (Jupyter, pandas, numpy, sklearn) with machine learning experience
  • Good understanding of using Slurm or similar parallel computing tools
  • Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, or related STEM field from top ranked University
  • Proficient in quantitative analysis, mathematical modelling, statistics, regression, and probability theory
  • Proficient in professional software development methodologies, version control systems, unit testing and debugging tools, and micro-services architecture
  • Excellent communication, problem-solving, and analytical skills, with the ability to quickly understand and apply complex concepts

Preferred Experience

  • 2+ years of experience in algorithmic trading systems development, preferably in systematic equity trading markets.
  • Experience working with and centralizing multiple vendor data sets
  • Experience collaborating effectively with cross functional teams, multitasking and adapting in a fast-paced environment

Highly Valued Relevant Experience

  • Entrepreneurial mindset
  • Ability to multitask and adapt
  • Curiosity and eagerness to learn and grow professionally
  • Self-motivated, detail-oriented, and able to work independently in a fast-paced environment

Target Start Date

ASAP

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Tips for Staying Inspired: How Data Science Pros Fuel Creativity and Innovation

Data science sits at the dynamic intersection of statistics, computer science, and domain expertise, driving powerful innovations in industries ranging from healthcare to finance, and from retail to robotics. Yet, the daily reality for many data scientists can be a far cry from starry-eyed talk of AI and machine learning transformations. Instead, it often involves endless data wrangling, model tuning, and scrutiny over metrics. Maintaining a sense of creativity in this environment can be an uphill battle. So, how do successful data scientists continue to dream big and innovate, even when dealing with the nitty-gritty of data pipelines, debugging code, or explaining results to stakeholders? Below, we outline ten practical strategies to help data analysts, machine learning engineers, and research scientists stay inspired and push their ideas further. Whether you’re just starting out or looking to reinvigorate a long-standing career, these pointers can help you find fresh sparks of motivation.

Careers

Top 10 Data Science Career Myths Debunked: Key Facts for Aspiring Professionals

Data science has become one of the most sought-after fields in the tech world, promising attractive salaries, cutting-edge projects, and the opportunity to shape decision-making in virtually every industry. From e-commerce recommendation engines to AI-powered medical diagnostics, data scientists are the force behind innovations that drive productivity and improve people’s lives. Yet, despite the demand and glamour often associated with this discipline, data science is also shrouded in misconceptions. Some believe you need a PhD in mathematics or statistics; others assume data science is exclusively about machine learning or coding. At DataScience-Jobs.co.uk, we’ve encountered a wide array of myths that can discourage talented individuals or mislead those exploring a data science career. This article aims to bust the top 10 data science career myths—providing clarity on what data scientists actually do and illuminating the true diversity and inclusiveness of this exciting field. Whether you’re a recent graduate, a professional looking to pivot, or simply curious about data science, read on to discover the reality behind the myths.

Careers

Global vs. Local: Comparing the UK Data Science Job Market to International Landscapes

How to evaluate salaries, opportunities, and work culture in data science across the UK, the US, Europe, and Asia Data science has proven to be more than a passing trend; it is now a foundational pillar of modern decision-making in virtually every industry—from healthcare and finance to retail and entertainment. As the volume of data grows exponentially, organisations urgently need professionals who can transform raw information into actionable insights. This high demand has sparked a wave of new opportunities for data scientists worldwide. In this article, we’ll compare the UK data science job market to those in the United States, Europe, and Asia. We’ll explore hiring trends, salary benchmarks, and cultural nuances to help you decide whether to focus your career locally or consider opportunities overseas or in fully remote roles. Whether you’re a fresh graduate looking for your first data science position, an experienced data professional pivoting from analytics, or a software engineer eager to break into machine learning, understanding the global data science landscape can be a game-changer. By the end of this overview, you’ll be better equipped to navigate the expanding world of data science—knowing which skills and certifications matter most, how salaries differ between regions, and what to expect from distinct work cultures. Let’s dive in.

🍪 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.