Quantitative Finance Analyst, Model Developer
4 weeks ago
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
Position Overview
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.
Required Skills:
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
Desired 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
Shift:
1st shift (United States of America)
Hours Per Week:
40
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