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Quantitative Data Scientist

Software Technology Inc Reston, VA

Reston, VA

Today

Full time Python Big Data Data Analysis

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Job Description

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 NumPy, 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

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