Employment Type: Permanent
About the Company
Join a fast‑growing metals trading business at the centre of global physical and financial commodities markets. We operate in a fast‑moving, data‑driven environment where analytical insight directly supports commercial decision‑making, risk management, and operational efficiency.
The Role
We are seeking a highly analytical and technically strong Data Analyst to support our trading, operations, and risk teams. You will be responsible for building automated workflows, enhancing data quality, and delivering actionable insights across market, pricing, and operational datasets. This is an excellent opportunity for someone who thrives in a trading‑floor environment and wants to make a visible impact in a rapidly expanding business.
Key Responsibilities
- Develop, maintain, and optimise Python‑based data pipelines and analytical tools (Pandas, NumPy, scikit‑learn).
- Build automated workflows to streamline data ingestion, validation, transformation, and reporting.
- Apply AI/ML techniques (forecasting, anomaly detection, NLP, classification models) to improve trading insight and operational processes.
- Support traders with real‑time and historical analysis of metals markets, price movements, supply‑demand patterns, and risk indicators.
- Produce clean, high‑quality datasets for trading, risk, logistics, and management reporting.
- Collaborate closely with trading, operations, and technology teams to enhance workflow efficiency and data reliability.
- Identify opportunities for automation to reduce manual tasks and improve decision‑making speed.
- Create dashboards, visual analytics, and data‑driven summaries for stakeholders across the business.
Skills & Experience Required
- Strong proficiency in Python (Pandas, NumPy; ML frameworks beneficial).
- Experience building automated data workflows, pipelines, or ETL processes.
- Exposure to machine learning, AI, or advanced analytics (time‑series, anomaly detection, forecasting, NLP etc.).
- Solid understanding of data quality, validation, and structured data modelling.
- Excellent communication skills with the ability to interpret complex data for non‑technical users.
- Commercial or academic exposure to commodities, trading, metals, or financial markets is advantageous (not essential).
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