Commodity Risk & Analytics Engineer

Full Description

Join a leading shipping analytics and optimisation company that’s transforming how global operators manage cost, logistics, and exposure.
We combine data science, operations research, and market analytics to help businesses make smarter trading and risk decisions.

🚀 The Mission
Build and extend our analytics and optimisation infrastructure to model and manage commodity price and exposure risk.
You’ll integrate high-frequency market data, develop risk and pricing models, and deliver insights through intuitive dashboards — turning complex market behaviour into clear, actionable intelligence.

🔧 What You’ll Do
Model Development: Design, implement, and maintain models for price dynamics, cross-commodity spreads, and exposure analytics using Python and modern numerical libraries (NumPy, Pandas, Pyomo, or similar).
Data Integration: Build robust data-ingestion pipelines for high-frequency market and OTC price feeds. Ensure data quality, latency monitoring, and transformation into model-ready formats.
Risk Analytics: Develop and deploy price-risk and sensitivity-analysis routines (delta/gamma tracking, scenario simulation, stress testing). Encode hedging logic and pricing relationships to support automated workflows.
Full-Stack Integration: Work with backend and frontend engineers to deliver APIs, endpoints, and real-time dashboards for risk and price visualisation. Collaborate with designers to communicate volatility, exposure, and correlation patterns clearly.
Operations Collaboration: Partner with optimisation and operations research teams to link market analytics with logistics and cost models, ensuring consistent risk attribution across the platform.
Client-Facing (Optional): Support enterprise clients by configuring data connections, validating analytics outputs, and aligning models with client risk methodologies.

🧠 What You’ll Bring
4–6 years’ experience in a quantitative, risk, or analytics engineering role
Strong Python skills (NumPy, Pandas, Pyomo) and familiarity with data APIs (REST / WebSocket)
Proven ability to model time-series and market relationships (regression, factor models, volatility, etc.)
Experience with backend services (Flask / FastAPI, Postgres, SQLAlchemy)
Solid understanding of risk management principles (exposure measurement, mark-to-market, VaR)
Ability to translate analytical outputs into engineering artefacts (APIs, dashboards, notebooks)

💰 Salary & Benefits
Competitive salary – expected range £75,000 – £90,000, depending on experience
Hybrid or fully remote working within the UK (occasional travel to London)
Opportunity to shape a next-generation analytics platform used across global shipping and commodity markets