Utilize Machine Learning methodologies, LLM and NLP techniques, and apply thoughtful quantitative, data science and analytical skills to solve complex business problems.
Develop risk strategies that improve risk monitoring capabilities through using various source.
Analyze structured/unstructured data from internal and external data sources to drive actionable insights in credit risk.
Lead development of event-driven scenario analytics to analyze the impact of macro-economic factors and current events on the Wholesale portfolio.
Develop data visualization and summarization techniques to convey key findings in dashboards and presentations to senior management.
Required qualifications, skills and capabilities
Advanced degree in analytical field (e.g., Data Science, Computer Science, Engineering, Mathematics, Statistics)
Excellent problem solving, communications, and teamwork skills.
Experience across broad range of modern analytic and data tools, Python/Anaconda, TensorFlow and/or Keras/PyTorch, Spark, SQL.
Financial service background or Credit Risk experience preferred, but not required.
Desire to use modern technologies as a disruptive influence within Banking.
Preferred qualifications
Deep understanding and practical expertise and/or work experience with Machine Learning. LLM/NLP expertise or experience is strongly preferred.