Financial Quantitative Engineer - Python Linux

This position will work on a project for one of the largest mortgage financing companies.

Key Job Functions:

• Develop, implement, and test all components using Python/Shell Scripts of financial models, algorithms, cash flow simulations and pricing/risk metrics calculations in end-user or production computing systems for business decisions, financial and regulatory reporting, and risk management.
• Use Advanced Analytics and Data Science techniques to efficiently translate complex mathematical, business, and financial modeling logic into software code. Design and execute test cases for modeling and analytical software applications to ensure they meet business needs and model requirements. Design and execute modeling application systems via distributed computing both on premise and on external cloud.
• Execute model application runs, process/validate model outputs, and produce/review quantitative reports for business use.
• Communicate complex quantitative analysis in a clear, precise, and actionable manner both verbally and in writing.

Key Qualifications:

• Proactive/fast learner and self-driven problem solver
• Strong software developer using Python (including SciPy, NumPy, and/or PySpark), SQL, and Linux Shell Scripting
• Familiarity with software development testing requirements, procedures, and tools
• Efficient in a Linux environment with a large volume (multi-million records) of data
• Strong mathematical and analytical skills
• Experience working in AWS/Cloud environment
• Experience implementing various quantitative models and algorithms
• Experience working with financial data, analytics, and cashflows applications
• Knowledge in Single-Family mortgage loans and derived products
• Knowledge of econometric modeling and statistical analysis.
• Knowledge of Monte Carlo simulations

Preferred Qualifications:

• MS in Computer Science, Statistics, Math, Engineering, or related field, PhD preferred
• 3 years of relevant experience in building large-scale model applications or systems
• Understanding of Credit Risk Model, Enterprise Regulatory Capital Framework (ERCF), Comprehensive Capital Analysis and Review (CCAR), Financial Statement Reporting (FSR), Credit Risk Transfer (CRT), MBS, etc. a big plus.
• Demonstrated skills with Jupyter Notebook, AWS Sagemaker, or Domino Datalab or comparable environments
• Knowledge in SAS, R, JAVA, C / C#, SQL, object oriented programming, service oriented architectures
• Knowledge of AI or machine learning techniques (regression, classification, clustering, graph models, etc.) is big plus
• Hands-on experience building models with deep learning frameworks like MXNet, Tensorflow, Keras, Caffe, PyTorch, Theano, or similar
• Experience search architecture (ex - Solr, ElasticSearch)