Quant Developer - Curves & Risk (Commodities)
$700–$770 year
On-sitePimlico, England, United Kingdom
Job Summary
Build the pricing and risk stack by engineering the curves construction engine and distributed VaR compute on AWS and Ray. Engineer dependency graphs for incremental curve recalculation and parallelize heavy numerical workloads to support intraday risk processing. Own cloud-native services with full observability and cost-awareness while partnering with desk quants to validate functional specifications. Drive development using in-house AI tooling to accelerate the build loop and generate tests. Deliver production-grade Python systems with rigorous testing and CI/CD, ensuring correctness for traders and reporters.
Required Qualifications
- 5–9 years building production software, with a track record of owning meaningful systems end-to-end
- Strong, idiomatic Python — performance-aware, well-tested, well-structured
- Comfortable with the numerical stack (NumPy/pandas or similar)
- Distributed / parallel compute experience — ideally Ray, but strong experience with another distributed framework (Dask, Spark, Celery, MPI, custom grids)
- Solid AWS — you've designed and run services in the cloud (compute, storage, containers, IaC)
- Real software-engineering maturity — system design, testing, CI/CD, observability, performance profiling
- Functional knowledge of commodities (in particular, oil and gas/LNG) — enough understanding of curves, pricing, P&L and risk (in oil, power and/or gas) to build the right thing and challenge a flawed requirement
- A challenging, ownership mindset — you ask why, you propose better, and you drive it through
- MUST have experience of oil and/or gas/LNG commodities trading environment
- MUST have current authorisation to work unrestricted in UK currently and for the next 12 months without need for sponsorship
- MUST be prepared to work onsite in central London 4 days/week
Desired Qualifications
- Hands-on Ray at cluster scale (Ray Core, Ray Data, autoscaling)
- Experience with VaR / risk engines
- Experience building curve construction or pricing libraries / DAG-based calculation engines
- Time-series and market-data pipelines at scale (exchange/broker feeds)
- Intraday / near-real-time risk or P&L experience
- Hands-on use of AI/LLM tooling in a development workflow
- A quantitative or numerical background
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