In this role, your work on the team will primarily be in applying advanced analytics to enable better commodity risk management decisions. For example, you might work as the lead in maintaining and expanding existing hedging strategies by re-training existing models through process driven approaches. You might also modify and improve algorithm performance across market regimes, by introducing new features, data sources, and modelling approaches; rapidly identify opportunities for our clients to increase earnings potential and reduce downside risk by back testing various risk management strategies; co-build bespoke tools with client data science teams that tailor machine-learning algorithms to attain an optimal balance of earnings and volatility given clients’ risk appetite and capital constraints; and/or collaborate with and train cross-functional client teams to instill long-lasting capabilities and ensure new decision-making models are embraced by organizations
Requirements:
Undergraduate degree is required; advanced degree in a quantitative discipline such as computer science (especially machine learning), applied mathematics, economics, quantitative finance or engineering is preferred or equivalent practitioner experience; 2+ years of commodity markets experience developing trading or hedging strategies (especially physical/cash markets) or price-discovery analysis in basic materials/metals, agriculture, softs, chemicals, plastics or oil & gas preferred; Experience writing clean, efficient Python code involving model development and deployment using state-of-the-art tools and libraries (e.g. scikit-learn, pandas, etc.)
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Senior Data Scientist - Commodity Trading and Risk Management In this role, your work on the team will primarily be in applying advanced analytics to enable better commodity risk management decisions. For example, you might work as the lead in maintaining and expanding existing hedging strategies by re-training existing models through process driven approaches. You might also modify and improve algorithm performance across market regimes, by introducing new features, data sources, and modelling approaches; rapidly identify opportunities for our clients to increase earnings potential and reduce downside risk by back testing various risk management strategies; co-build bespoke tools with client data science teams that tailor machine-learning algorithms to attain an optimal balance of earnings and volatility given clients’ risk appetite and capital constraints; and/or collaborate with and train cross-functional client teams to instill long-lasting capabilities and ensure new decision-making models are embraced by organizations Undergraduate degree is required; advanced degree in a quantitative discipline such as computer science (especially machine learning), applied mathematics, economics, quantitative finance or engineering is preferred or equivalent practitioner experience; 2+ years of commodity markets experience developing trading or hedging strategies (especially physical/cash markets) or price-discovery analysis in basic materials/metals, agriculture, softs, chemicals, plastics or oil & gas preferred; Experience writing clean, efficient Python code involving model development and deployment using state-of-the-art tools and libraries (e.g. scikit-learn, pandas, etc.)
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