Quantitative Data Scientist
- 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