Aflac, a Fortune 500 company, is looking to develop and empower its people to cultivate relationships, give back to the community, and celebrate every success along the way, by solving business problems related to voluntary insurance products that pay cash directly to policyholders.
Requirements
- Knowledge of standard quantitative finance models (and stochastic calculus) on a practical level.
- Deep orientation toward probabilistic thinking; Bayesian mindset.
- Appreciation for the financial meaning and economic basis of models.
- Familiarity with major fixed income asset classes, basic bond math, Vanilla FX/IR derivatives.
- Knowledge of source control (Git/GitHub), databases (Snowflake), visualization tools (Plotly/Dash) and Big Data / ML frameworks (Scikit-learn, Apache Spark) is a plus.
- Python, C++, SQL or similar experience is preferred.
- Advanced degree
Responsibilities
- Contributes to the development and calibration of models concerning: Hedging strategies and optimization in FX, IR, credit, and equities domains;
- Supports capital measurement and regulatory analytics (US and Japan focus) with potential exposure to other regulatory jurisdictions and rating agencies.
- Manages longer-term projects associated with building out key functionality in the platform.
- Provides tactical analysis using existing tools, with immediate impact on business decisions.
- Explains modeling methodology and model results to non-quants within the business.
- Performs other duties as assigned.
- Tactical asset allocation
Other
- Acting with Integrity
- Communicating Effectively
- Pursuing Self-Development
- Serving Customers
- Supporting Change
- Bachelor's degree Mathematics, quantitative finance, physics, financial engineering, computer science or other related field.
- 1+ years of relevant hands-on programming experience involving large projects, implementation of quantitative models, and intuition for how to write usable, and understandable code.
