Zhi Zheng is a data-savvy analyst with seven years of practical experience applying statistical modeling, SQL, Python and R to financial risk, credit assessment and anti-money laundering (AML) problems. He has interned at Ernst & Young, Zheshang Securities and Hangzhou Financial Investment Group, where he merged domain knowledge with machine learning techniques such as Random Forests and SVMs to improve AML systems and model bond and loan risk. A University of Nottingham mathematics graduate with a 4.0 GPA, he translates complex quantitative methods into clear business recommendations and feasibility reports for non-technical stakeholders. Beyond typical analyst work, he has hands-on experience designing data models for foreign exchange and running principal component and factor analyses that directly informed investment and underwriting decisions. Based in Nottingham, he combines rigorous academic training with field-tested consulting experience across finance and large-event sponsor operations.
7 years of coding experience
Bachelor of Science - BS, Mathematics with Applied Mathematics, GPA 4.0/4.0, Bachelor of Science - BS, Mathematics with Applied Mathematics, GPA 4.0/4.0 at University of Nottingham
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