Quantitative Data Developer

Two Quantitative Data Developers for the models and quantitative data team at a global trading and risk SaaS vendor. You'll build the Python, SQL and Snowflake data solutions that feed pricing models and real-time market risk across every asset class. The data has to be right, because global investment banks and multi-strategy hedge funds price and manage risk on it.The workTwo hires into the London models and quantitative data team, working alongside the quant developers. You'll discover, design, build and maintain the data solutions behind position valuation and the datasets the models depend on: curves, volatility cubes and correlation matrices. You'll also help build the data-driven systems that calculate market risk, VaR and greeks, in real time across equity, credit, FX, fixed income, commodities, crypto and their derivatives. It suits someone who enjoys turning large, raw datasets into inputs a model can trust, and brings the same rigour to the maths. The role is London-based, four days a week in the office. Remote working is not possible.What you'll be doingWork with the quant developers on major projects to make data pipelines and analytics infrastructure faster and more reliableDesign and build data solutions that process and deliver inputs for pricing models and market risk calculations across asset classesBuild and run high-performance, scalable data applications and tools in Python, SQL and Snowflake to analyse, transform and check large financial datasetsDocument data methodologies clearly, to support internal and external validationWhat you'll needAn MS or PhD in mathematics, physical sciences or engineering is preferred.3 to 5 years of large-scale Python development and SQL on data-intensive productsStrong quantitative foundations: numerical methods, linear algebra, PDEs, probability and statisticsAn understanding of financial derivatives, market conventions and their implementation. This one is a mustProficiency with yield curves (OIS, Libor, cross-currency), inflation curves, volatility surfaces and interest rate volatility cubes, ideally with live or intraday dataC++ or Java alongside PythonRisk tooling experience, such as VaR, Monte Carlo, scenario analysis and P&L, is a plusWhat happens nextWe start with a confidential conversation to walk through the team, the data and the problems you would be working on before anything moves forward.