Quantitative Researcher

Radley James is working with a growing financial technology and capital markets firm that develops quantitative trading technology and analytics solutions for institutional fixed income markets. Their research team works closely with traders and engineers to build pricing models, execution algorithms and data-driven tools that directly impact trading performance and client outcomes.This is a junior-level opportunity suited to candidates with approximately 1–5 years of experience. The team is particularly interested in individuals who have worked in small, high-impact environments where they have taken ownership of projects and contributed directly to production systems. Candidates from fintechs, electronic trading teams, quantitative research backgrounds or strong academic programmes are encouraged to apply.ResponsibilitiesDevelop and enhance quantitative models supporting fixed income trading technology and corporate bond pricingWork with large time series datasets to improve predictive and analytical modelling capabilitiesDesign and implement execution algorithmsBuild data analytics and decision-support tools used by trading and sales teamsCollaborate closely with researchers, traders and engineers to deliver production-ready solutionsRequirementsDegree in Finance, Computer Science, Engineering, Mathematics, Physics, Data Science or a related quantitative discipline from a leading university1–5 years of experience in quantitative research, data science, machine learning, electronic trading or a related fieldStrong Python development skills with experience writing high-quality production codeExperience working with time series data, forecasting, prediction models or statistical modelling techniquesStrong knowledge of NumPy, Pandas, PyArrow and machine learning librariesSelf-motivated, analytical and comfortable working with a high degree of ownership and autonomyPhD candidates, recent graduates with strong internships, and candidates from fintech or electronic trading environments are all encouraged to applyTechnology StackPythonAWSKubernetes (EKS)DockerKafkaGitLabPrometheusReact