Quantitative Researcher - Metals

Quantitative Researcher – Metals

 Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology and trading expertise has shaped QRT’s collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors.

Your future role

The role combines fundamental and quantitative research, translating supply & demand analysis into actionable trading opportunities, alongside modelling, dashboard development and trade execution.

Develop proprietary physical-market datasets, supply & demand balance sheets and quantitative models for base metals, translating supply, demand, inventory, flow and commercial-incentive analysis into fair-value assessments and actionable trading research.

 

Your responsibilities include:

• Your core objective is to create high-quality predictive signals.

 

1. US copper cathode balance sheet and vessel tracking

• Own the monthly US refined-copper balance, covering domestic production, trade flows, end-use demand and reported and estimated inventories.
• Maintain a vessel tracking model for cathode shipments to the US and reconcile cargo origin, tonnage, and arrival timing with customs, bill-of-lading, and official trade data.
• Derive fair value models for CME copper market.

1. Base-metals raw-material balances

• Develop copper and zinc raw-material balances covering
  • copper concentrates, blister and anode, copper scrap,
  • zinc concentrates, Waelz oxide and zinc scrap.
• Integrate mine and secondary supply, smelter and refinery utilization rates, trade, inventories, smelter margins and processing constraint into models for secondary feed ratios, recovery rates and assay estimates.
• Ensure plausibility of implied inventory evolution in context of commercial incentives.

1. End-use demand modelling

• Develop end-use demand models using downstream production, installations, shipments, inventory and other relevant industry data.
• Maintain detailed coverage of AI Data Centers, New Energy Vehicles (NEVs) and Power Generation Equipment across Wind, Solar, Hydro, Coal and Gas.
• Model intensity of use across metals and scrap use, reflecting substitution dynamics and efficiency gains.
• Reconcile modelled end-use demand with apparent consumption, company disclosures and other observable industry indicators.

1. Balance reconciliation and commercial incentives

• Reconcile balance-sheet outputs with visible stock time series; explain gaps arising from timing, classification, unreported stocks and data revisions.
• Maintain incentive and arbitrage series combining physical premia, exchange prices and spreads, freight, interest, insurance, duties, warehouse queues, loading-out rates and other logistical costs.
• Distinguish executable from indicative economics and use commercial incentives to validate expected trade flows, inventory movements and balance conclusions.

1. Fair-value analysis for exchange-traded commodities

• Develop fair-value frameworks for exchange-traded base metals using output from proprietary balance sheet time series and incentive time series (see above).
• Evaluate outright prices, time spreads and cross-market relationships; communicate fair-value ranges, sensitivities, uncertainty and relevant catalysts.

1. Quantitative research and strategy support

• Support research across base metals through data preparation, feature engineering, signal research, backtesting, scenario analysis and performance attribution.
• Convert market hypotheses into robust datasets and reproducible tests, controlling for revisions, look-ahead bias and changes in market regime.

1. Data infrastructure and research communication

• Automate recurring collection, validation, reconciliation and reporting using Python, SQL, APIs and other appropriate tools.
• Maintain version-controlled code, reproducible calculations, source metadata, assumption histories and data-quality checks.
• Provide concise updates on balance revisions, fair-value signals, model limitations and material risks; share reusable tools and methods across the team.
• Develop discretionary trading strategies across futures and options for implementation by the Portfolio Manager.

 

Your present skillset

• Advanced degree in a quantitative field such as data science, statistics, mathematics, physics or engineering.
• Strong knowledge in statistics, machine learning, NLP and AI techniques.
• Strong understanding of derivative markets including options valuation techniques.
• In-depth knowledge of base metals markets, including delivery mechanisms (CME, LME and SHFE). Experience of warrant sifting as well physical origination and marketing.
• Knowledge of major Asian languages desirable.
• Capacity to multi-task in a fast-paced environment while keeping strong attention to detail.
• Advanced coding skills required in Python.
• Strong capacity to communicate with technologists, data scientists and traders across the globe.

 

QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work-life balance.