National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

Quantitative Developer, Systematic Equities

Millennium Management
London
5 months ago
Applications closed

Related Jobs

View all jobs

ETL Test

Senior Principal Data Scientist

Data Analytics Manager

Senior Market Data Engineer

Product Data Scientist - hybrid

Data Scientist

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

National AI Awards 2025

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.

Part-Time Study Routes That Lead to Machine Learning Jobs: Evening Courses, Bootcamps & Online Masters

Machine learning—a subset of artificial intelligence—enables computers to learn from data and improve over time without explicit programming. From predictive maintenance in manufacturing to recommendation engines in e-commerce and diagnostic tools in healthcare, machine learning (ML) underpins many of today’s most innovative applications. In the UK, demand for ML professionals—engineers, data scientists, research scientists and ML operations specialists—is growing rapidly, with roles projected to increase by over 50% in the next five years. However, many aspiring ML practitioners cannot step away from work or personal commitments for full-time study. Thankfully, a rich ecosystem of part-time learning pathways—Evening Courses, Intensive Bootcamps and Flexible Online Master’s Programmes—empowers you to learn machine learning while working. This comprehensive guide examines each route: foundational CPD units, immersive bootcamps, accredited online MSc programmes, funding options, planning strategies and a real-world case study. Whether you’re a software developer branching into ML, a statistician aiming to upskill, or a professional exploring AI-driven innovation, you’ll discover how to build in-demand ML expertise on your own schedule.

The Ultimate Assessment-Centre Survival Guide for Machine Learning Jobs in the UK

Assessment centres for machine learning positions in the UK are designed to reflect the complexity and collaboration required in real-world ML projects. From psychometric assessments and live model-building tasks to group data science challenges and behavioural interviews, recruiters evaluate your statistical understanding, coding skills, communication and teamwork. Whether you specialise in deep learning, reinforcement learning or NLP, this guide offers a step-by-step approach to excel at every stage and secure your next ML role.

Top 10 Mistakes Candidates Make When Applying for Machine-Learning Jobs—And How to Avoid Them

Landing a machine-learning job in the UK is competitive. Learn the 10 biggest mistakes applicants make—plus tested fixes, expert resources and live links that will help you secure your next ML role. Introduction From fintechs in London’s Square Mile to advanced-research hubs in Cambridge, demand for machine-learning talent is exploding. Job boards such as MachineLearningJobs.co.uk list new vacancies daily, and LinkedIn shows more than 10,000 open ML roles across the UK right now. Yet hiring managers still reject most CVs long before interview—often for avoidable errors. Below are the ten most common mistakes we see, each paired with a practical fix and a live resource link so you can dive deeper.