Quantitative Developer, Systematic Equities.

Millennium Management
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist (Applied AI)

Senior Data Scientist

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

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 tradingAssist in designing, coding, and maintaining tools for the systematic trading infrastructure of the teamBuild and maintain robust data pipelines and databases that ingest and transform large amounts of dataDevelop 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

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.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.