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
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
1st shift (United States of America)