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Downstream Demand Analyst (Metals)

ShanghaiOn-site

AI Summary

Role Summary Build and maintain data-driven end-use demand models across global metals markets, translating sector-level insights into actionable views for trading and investment.

About this role

Role Summary

Build and maintain data-driven end-use demand models across global metals markets, translating sector-level insights into actionable views for trading and investment.

Core Responsibilities

  1. Demand Modelling
  • Develop bottom-up end-use demand models (starting with China, then globally and covering major regional markets) across key sectors (e.g. real estate, transportation, power generation and distribution, infrastructure, data centres / AI, appliances).
  • Conduct intensity analysis and forward demand projections, adjusting for cyclical effects (e.g. demand destruction vs deferral).
  • Track inventories across the value chain using semi-finished and end-use data.
  • Analyse capacity across end-use sectors to consume scrap vs refined metals.
  1. Data Integration & Monitoring
  • Build scalable data pipelines integrating national statistics, industry data, company disclosures, and alternative data (e.g. shipping, customs, satellite).
  • Generate model outputs programmatically, incorporating real-time and high-frequency macro data.
  • Develop dashboards to present key high-frequency indicators in a clear, actionable format.
  1. Market Analysis & Insights
  • Identify market inefficiencies and relative value opportunities across commodities and regions.
  • Perform scenario analysis incorporating macro, policy, and geopolitical factors.
  • Support trading and portfolio positioning with timely, data-driven insights.

Key Requirements

Experience & Knowledge

  • 5+ years’ experience in metals/commodity research, with strong focus on first-use and end-use demand.
  • Deep understanding of downstream sectors and global metals value chains.
  • Familiarity with scrap markets, trade flows, and industry dynamics.

Technical Skills

  • Advanced Python programming (model development, data pipelines).
  • Strong SQL proficiency.
  • Experience working with large, multi-source datasets and real-time data systems.

Other Skills

  • Strong analytical and problem-solving capabilities.
  • Ability to operate in a fast-paced, collaborative environment.
  • Excellent communication skills (written and verbal, English).

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