Master’s in Data Science, Computer Science, Applied Math, or Financial Engineering; or Bachelor’s in same fields with 5+ years of quantitative model development experience in Python, SQL.

Technical Skills

Proficiency in Python with strong experience using quantitative/statistical packages (NumPy, pandas, SciPy, statsmodels, scikit-learn, QuantLib).

Strong SQL skills for working with large mortgage/loan datasets.

Ability to design, implement, and optimize Monte Carlo simulations and time-series models.

Experience building, testing, and maintaining production-ready Python/Shell code with Git, unit testing, and CI/CD.

Hands on experience with AWS services like Amazon S3, AWS Lambda, AWS Batch, AWS Glue, AWS EMR, Cloudwatch and IAM , EC2

Quantitative Modeling Knowledge

Familiarity with Potential Future Exposure (PFE) methodologies for counterparty credit risk.

Understanding of interest rate modeling using time series techniques.

Basic understanding of derivative pricing and exposure dynamics.

Exposure to macro risk factor models relevant to mortgage portfolios.

Soft Skills

Strong analytical and problem-solving skills with attention to detail.

Ability to clearly communicate results and technical design to both modelers and business stakeholders.

Focused on manipulating data in a software engineering capacity. Some of that data might live in relational systems, but it's increasingly moving towards NoSQL systems and data lakes. Normalize databases and ascertain the structure of the data meets the requirements of the applications that are accessing the information. Construct datasets that are easy to analyze and support company requirements. Combine raw information from different sources to create consistent and machine-readable formats. Skills: This IT role requires a significant set of technical skills, including a deep knowledge of SQL, data modeling, and tools like Spark/Hive/Airflow.

CBC

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