Quantitative Data Scientist
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Strong proficiency in Python, especially with libraries like NumPy, pandas, SciPy, statsmodels, scikit-learn, and QuantLib.
Advanced SQL skills for handling large and complex mortgage/loan datasets.
Experience designing and optimizing Monte Carlo simulations and time-series models.
Solid understanding of counterparty credit risk, including Potential Future Exposure (PFE) methodologies.
Familiarity with interest rate modeling, derivative pricing, and macro risk factor models.
Hands-on experience with AWS services such as S3, Lambda, Batch, Glue, EMR, CloudWatch, IAM, and EC2.
Competence in software engineering practices including Git, unit testing, CI/CD, and shell scripting.
Experience working with data lakes, NoSQL systems, and tools like Spark, Hive, and Airflow.
Strong analytical thinking and attention to detail.
Ability to communicate complex technical concepts clearly to both technical and non-technical audiences.Minimum 5 years of experience in quantitative modeling, data engineering, or a related field (if holding a Bachelor's degree).
Education:
Bachelor's degree in Business Administration, Information Systems, Computer Science, or a related field.
Skills:
Business Analysis
Shell Script
SQL
NoSQL