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# Quantitative Data Engineer

- **Company:** G-Research
- **Location:** London, UK
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Structures, Python (Programming Language), Operational Databases, Software Engineering, Data Processing, Large Language Models, Information Technology
- **Published:** October 22, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=cb19cc11f71a51c9

## About the Role

The ideal candidate will have the following skills and experience:

 * A strong academic record, including a minimum 2:1 degree or equivalent in a quantitative subject such as mathematics, statistics, computer science, physics or engineering
 * A combination of strong software engineering and data skills
 * Solid knowledge of algorithms, data structures and software fundamentals
 * Experience building production data pipelines, with strong Python data processing skills
 * Comfort applying probability and statistics to real-world data problems
 * Motivation from seeing your work deliver measurable value
 * A preference for highly collaborative environments that value innovation, proactivity and flexibility
 * Hands-on experience of LLM-based agents and workflows is desirable
 * You don't need to have all of the above to apply. Finance experience is not necessary for this role and candidates from non-financial backgrounds are encouraged to apply.

## Description

Quantitative Data Engineers take data from its raw source through to live signals, working alongside Quantitative Researchers at each step in between.

We are using large language models to automate and scale this process, so that we can take on more data than any team could handle by hand. The work is already underway. You will work in Python on large-scale compute and industry-leading infrastructure, with access to leading internal and external models.

AI is part of how every team here works, accelerating thinking and delivering impact. All colleagues are expected to bring AI into their daily workflows., * Onboarding new third-party datasets, building the pipelines, normalisation and validation that make them research-ready

 * Partnering with Quantitative Researchers to engineer features and build signals from onboarded data
 * Using LLM-based agents and workflows to safely automate the pipeline from ingestion to signals
 * Building tools and reusable workflows that help researchers and data scientists scale their work