Quantitative Data Scientist role at Software Technology Inc in Reston, VA onsite, focusing on Python based modeling, risk analytics, and data engineering with AWS and big data tooling.
Responsibilities
- Leverage advanced Python expertise across core libraries like NumPy, pandas, SciPy, statsmodels, scikit-learn, and QuantLib to design and maintain quantitative models.
- Operate on large and complex mortgage and loan datasets with expert SQL skills to support analytics workflows.
- Design, calibrate, and optimize Monte Carlo simulations and time series models used for risk assessment.
- Apply counterparty credit risk concepts, including Potential Future Exposure methodologies, within modeling work.
- Model interest rate dynamics, derivative pricing, and macro risk factor influences within analytical frameworks.
- Develop and sustain data pipelines and analytics on AWS using S3, Lambda, Batch, Glue, EMR, CloudWatch, IAM, and EC2.
- Adopt software engineering practices such as Git version control, unit testing, CI/CD pipelines, and shell scripting to ensure reliable deliverables.
- Work with data lakes, NoSQL systems, and orchestration tools like Spark, Hive, and Airflow to enable scalable data processing.
- Demonstrate strong analytical thinking and meticulous attention to detail across modeling tasks.
- Communicate intricate technical concepts clearly to both technical and non-technical audiences.
- Meet a minimum of five years of experience in quantitative modeling, data engineering, or related fields, with a Bachelor's degree as the educational baseline.
Requirements
- Strong Python proficiency with libraries such as NumX, pandas, SciPy, statsmodels, scikit-learn, and QuantLib.
- Advanced SQL skills for handling large and complex mortgage or loan datasets.
- Experience designing and optimizing Monte Carlo simulations and time-series models.
- Solid understanding of counterparty credit risk, including Potential Future Exposure methodologies.
- Familiarity with interest rate modeling, derivative pricing, and macro risk factor models.
- Hands-on experience with AWS services including S3, Lambda, Batch, Glue, EMR, CloudWatch, IAM, and EC2.
- Competence in software engineering practices such as 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 five years of experience in quantitative modeling, data engineering, or a related field, with a Bachelor's degree required.
Technologies
- Python
- NumPy
- pandas
- SciPy
- statsmodels
- scikit-learn
- QuantLib
- SQL
- S3
- Lambda
- Batch
- Glue
- EMR
- CloudWatch
- IAM
- EC2
- Git
- Spark
- Airflow
- NoSQL
- Shell scripting
- CI/CD
Skills
- Business Analysis
- Shell Script
- SQL
- NoSQL