Squarepoint Capital

Manages quant-driven, systematic portfolios globally

Graduate Quant Developer

Quantitative DevelopmentConfirmed live in the last 24 hours

Full-timeNo salary listed

About the Job

Requirements

Degree in a quantitative discipline (e.g. Mathematics, Physics, Engineering, Computer Science, or similar) with a strong interest in computational and software-driven problem solving
Proficiency in at least one of our core languages: Python, C++, or KDB+/q
Prior exposure to software development in a commercial, academic, or research environment, evidenced through internships, academic projects, work experience, or personal projects
Familiarity with Linux and version control systems (e.g. git)
Ability to work effectively with AI tools and systems, including demonstrable experience using them collaboratively to enhance software development, data analysis, or research workflows
Clear written and verbal communication skills
Strong curiosity, motivation to learn, and willingness to develop new skills
Ability to work effectively in a fast-paced, collaborative team environment

Responsibilities

Apply quantitative reasoning and software engineering skills to support investment research and trading
Learn to translate business and research requirements into scalable, maintainable data processing workflows
Work closely with quantitative researchers to design and implement efficient workflows for alpha generation
Contribute to data-driven analytics and tooling across a wide range of asset classes
Develop, extend, and maintain libraries, tools, and frameworks used by researchers and traders globally
Collaborate closely with peers across our global Investment and Technology departments
Gradually take ownership of technical components and contribute ideas and solutions with direct commercial impact

Desired Qualifications

Proficiency in more than one of our core programming languages
Exposure to quantitative finance, trading, or financial markets through coursework, projects, or internships
Previous experience in a financial services or technology-driven environment
Hands-on experience using git and Linux in team-based or academic projects

Please only apply to the one job you feel best fits your skillset and experience. If our team feels you are better suited for another role, we will reach out about the alternate opportunity.

Position Overview:

Squarepoint is seeking talented, curious, and self‑driven graduate‑level candidates who are eager to apply strong quantitative and computational foundations in a challenging and innovative environment at the intersection of technology and investment research.

As a Graduate Quant Developer, you will work on the front line of our quantitative research business, collaborating closely with quantitative researchers and technologists to design, build, and maintain scalable, production‑grade systems that directly support alpha generation and investment decision‑making. This role is designed to support early‑career growth through hands‑on development, close collaboration, and continuous learning in a highly collegiate, high‑performance environment.

Role and Responsibilities

Apply quantitative reasoning and software engineering skills to support investment research and trading
Learn to translate business and research requirements into scalable, maintainable data processing workflows
Work closely with quantitative researchers to design and implement efficient workflows for alpha generation
Contribute to data‑driven analytics and tooling across a wide range of asset classes
Develop, extend, and maintain libraries, tools, and frameworks used by researchers and traders globally
Collaborate closely with peers across our global Investment and Technology departments
Gradually take ownership of technical components and contribute ideas and solutions with direct commercial impact

Required Qualifications

Degree in a quantitative discipline (e.g. Mathematics, Physics, Engineering, Computer Science, or similar) with a strong interest in computational and software‑driven problem solving
Proficiency in at least one of our core languages: Python, C++, or KDB+/q
Prior exposure to software development in a commercial, academic, or research environment, evidenced through internships, academic projects, work experience, or personal projects
Familiarity with Linux and version control systems (e.g. git)
Ability to work effectively with AI tools and systems, including demonstrable experience using them collaboratively to enhance software development, data analysis, or research workflows
Clear written and verbal communication skills
Strong curiosity, motivation to learn, and willingness to develop new skills
Ability to work effectively in a fast‑paced, collaborative team environment

Nice to Have

Proficiency in more than one of our core programming languages
Exposure to quantitative finance, trading, or financial markets through coursework, projects, or internships
Previous experience in a financial services or technology‑driven environment
Hands‑on experience using git and Linux in team‑based or academic projects

About the Company

Squarepoint Capital

Manages quant-driven, systematic portfolios globally

Squarepoint Capital is a global investment manager that uses quantitative and systematic strategies to build diversified portfolios across asset classes, trading frequencies, and global markets for institutional investors. Its approach relies on a large data platform and advanced technology to power research and model-based trading decisions, with portfolios continually adjusted in response to market data. The firm differentiates itself through a broad, multi-asset, multi-frequency framework, a global footprint with offices in thirteen cities, and a collaborative culture that emphasizes rigorous research and disciplined execution. Its goal is to deliver attractive, risk-aware returns for pension funds, endowments, and sovereign wealth funds through systematic, data-driven investment processes.

Company size: 501-1,000
Company stage: Not listed
Total funding: $1.4B
Headquarters: London, United Kingdom
Founded: 2014

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