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Downstream Demand Analyst (Metals)

Qube Research & Technologies
CompanyQube Research & Technologies
CategoryData & Analytics
LocationShanghai
RemoteOn-site (inferred)
EmploymentNot stated
LevelNot stated
SalaryNot stated by the employer
Posted15 Jun 2026
Last verified30 Jul 2026
SourceEmployer career page (greenhouse)
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Description
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   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.     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.   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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