Commodities Quantitative Platform Engineer

United States Digital Space LLC

Greater London

On-site

GBP 90,000 - 130,000

Full time

6 days ago
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Job summary

United States Digital Space LLC in London seeks a Quantitative Analyst for the Commodities team to develop and support platforms for investible indices and analytics. The role focuses on building scalable systems, backtesting tools, and production-grade workflows in a fast-paced, business-facing environment.

You will work with trading, index structuring, sales and technology to deliver timely insights and robust performance across GCIP initiatives.

Qualifications

  • A strong degree in mathematics, statistics, CS, engineering, physics, quantitative finance, or a related field.
  • Strong Python programming and software development skills.
  • Experience with structured development practices, including source control, testing, code review, and release processes.
  • Rigorous problem-solving skills and data quality focus.
  • Interest in production-quality quantitative platforms and business-facing roles.

Responsibilities

  • Develop and maintain the Python platform supporting commodities investible indices.
  • Implement new indices, strategies, backtesting capabilities, and trading analytics.
  • Build scalable data pipelines, calculation components, controls, and monitoring tools; support production operations.

Skills

Python programming
Software development
Problem solving
Communication skills

Education

Mathematics / Statistics / CS / Engineering / Physics / Quant Finance degree

Tools

SQL
kdb+/q
APIs
Large-scale data processing

Job description

United States Digital Space LLC in London seeks a Quantitative Analyst for the Commodities team to develop and support platforms for investible indices and analytics. The role focuses on building scalable systems, backtesting tools, and production-grade workflows in a fast-paced, business-facing environment.

You will work with trading, index structuring, sales and technology to deliver timely insights and robust performance across GCIP initiatives.

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