Quantitative Developer

NextGen Staffing
Charing Cross, United Kingdom
2 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior
Compensation
£ 220K

Job location

Charing Cross, United Kingdom

Tech stack

Big Data
C++
Nvidia CUDA
Software Debugging
Python
NumPy
TensorFlow
Data Ingestion
PyTorch
Pandas
Plotly
Data Pipelines

Job description

We're working with a globally recognised, technology-driven trading firm at the forefront of systematic execution and quantitative research, operating across multiple asset classes and markets. They're now looking for a Senior Quantitative Developer to join a high-impact team responsible for building a next-generation alpha research platform.

This is not just another quant dev role - you'll be working on the core infrastructure that powers alpha generation, from data ingestion and processing through to model evaluation, reporting, and deployment. The team is focused on solving complex problems around execution quality, market impact, and data-driven decision-making, using cutting-edge tools and large-scale datasets.

You'll work closely with quants and researchers, influence architecture decisions, and play a key role in shaping how strategies are built and tested across the business.

Requirements

  • Strong experience with Python (5+ years) in a quantitative finance environment
  • Experience building data pipelines, research platforms or ML workflows
  • Strong understanding of statistics, linear models & model evaluation techniques
  • Experience with the Python data stack (NumPy, Pandas, etc.) and ML libraries
  • Familiarity with tools such as PyTorch, TensorFlow, JAX, Plotly, Altair
  • Experience working with large datasets and performance-sensitive systems
  • Strong problem-solving ability with attention to detail
  • Ability to communicate complex technical ideas clearly

Nice to have:

  • Experience with C++, Rust or CUDA
  • Experience optimising or debugging low-level performance in data/ML systems

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