Quantitative Developer

Required Skills & Experience
• Master’s degree in quantitative field (e.g. mathematics, physics, statistics, computer science/computer engineering, financial engineering, etc.).
• 6+ years of risk management experience.
• Solid programming skills and experience with statistical and data analysis, modelling techniques and numerical implementations. More specifically experience in Python/C++, Perl, shell scripts in Linux environment and basic database skills in either Oracle or Sybase/SQL.
• Working knowledge of a compiled language like C/C++/Java.
• Exposure to numerical libraries and data processing.
• Ability for abstraction and conceptualization, reasoning about program behavior at different levels of abstraction from hardware to applications.
• Keen interest in banking and finance, especially in the field of Risk Management.

Desired Skills & Experience
• PhD, a second Master’s degree, CPA, FRM or CFA
• Experience in quantitative finance or a related field, analyzing large and complex data sets, data reliability analysis, quality controls and data processing.
• Experience derivative pricing and exotic products; risk management practices and procedures; numerical methods; Monte Carlo simulations; statistical hypotheses testing; trading-book products.

What You Will Be Doing
• Partners include various working groups, model developers, risk managers, business clients, model validators, Risk IT, internal and external auditors, and regulators. Engage with partners, as appropriate, to:
• Develop and implement methodologies, algorithms and diagnostic tools for testing model robustness, stability, reliability, performance and quality control of modeling data.
• Enhance efficiency and effectiveness of implementation of post model development analytics.
• Automate and consolidate ongoing model analysis and the annual model review process across different models.
• Migrate analytics to a production environment as appropriate.
• Support various tasks in response to regulatory and internal risk management requirements.
• Develop, maintain and enhance technical documentation including project plans, model descriptions, mathematical derivations, data analysis, process and quality controls.
• Design and implement a framework for model-driven computations on a graph.
• Design and implement a model library for model performance testing.
• Unit testing, reliability, and improving the quality of compute pipelines.
• Learn about Python, its ecosystem, community, and best practices.
• Generate ideas to improve the model and data platform and assisting in their implementation.