Quantitative Finance Analyst, Model Developer - Global Financial Crimes

Job Description:

As a Quantitative Finance Analyst on the Enterprise Risk Analytics team, your main responsibilities will involve:

• Applying quantitative methods to develop capabilities that meet line of business, risk management and regulatory requirements
• Maintaining and continuously enhancing capabilities over time to respond to the changing nature of portfolios, economic conditions and emerging risks
• Understanding and executing activities that form the end-to-end model development and use life cycle
• Clearly documenting and effectively communicating quantitative methods as part of ongoing engagement with key stakeholders, including the lines of business, risk managers, model validation, technology

• Responsible for independently conducting quantitative analytics and modeling projects.
• Responsible for developing new models, analytic processes or systems approaches.
• Creates documentation for all activities and works with Technology staff in design of any system to run models developed.
• Incumbents possess excellent quantitative/analytic skills and a broad knowledge of financial markets and products.
• This role will support the AML Event Processor and respective team. The Event Processor is a consolidation engine that aggregates events from all detection channels and calculates aggregated risk scores for event groups based on individual risk factor scores.

• Graduate degree in quantitative discipline (e.g. Mathematics, Economics, Engineering, Finance, Physics)
• 2+ years of experience in model development, statistical work, data analytics or quantitative research or PhD
• Strong Programming skills e.g. R, Python, SAS, SQL or other languages
• Strong analytical and problem-solving skills

• Knowledge of predictive modeling, statistical sampling, optimization, machine learning and artificial intelligence techniques
• Strong technical writing, communication and presentation skills and ability to effectively communicate quantitative topics with non-technical audiences
• Experience with large data sets
• Effective at prioritization/time and project management
• Broad understanding of financial products