Elnaz is the Sole Data Scientist and Senior Quantitative Business Analyst for physical trading activities for a Governmental institution within the MENA region. Following her PhD in Robotics and Modelling at Imperial College and her postdoc in Machine Learning, she became a Quantitative researcher in climate finance. Her current interests lie in the applications of machine learning in decision modeling and data mining and aggregation challenges within the Middle East’s downstream market.

Commodities & Data: Applications of Machine Learning in the Downstream Market
Recent political events and global economic volatility highlight the uncertainty of previously accepted deterministic models in analyzing downstream commodity transactions and need for case specific modelling. Data mining and machine learning have the advantage of removing econometric assumptions, however, the lack of integral data creates a data mining and classification challenge, explained within this presentation.

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