Quantitative Data Scientist

Software Technology Inc; Reston (VA); On-site; USD 120,000 - 180,000; Full time

Job summary

Software Technology Inc. in Reston, VA is seeking a Quantitative Data Scientist to build Python-based models for risk analytics and data engineering with AWS and big data tooling.

You'll leverage NumPy, pandas, SciPy, and scikit-learn to design and calibrate models on large mortgage and loan datasets, developing scalable data pipelines and applying advanced risk concepts. This onsite role emphasizes collaboration, rigorous testing, and clear communication of complex results.

Qualifications

• 5+ years in quantitative modeling or data engineering.
• Strong Python and SQL skills for large datasets.
• Experience with Monte Carlo, time-series, and risk modeling.
• Experience with AWS data tooling and data pipelines.

Responsibilities

• Design and maintain quantitative models using Python libraries.
• Work with large mortgage and loan datasets using SQL.
• Calibrate Monte Carlo simulations and time-series models for risk.
• Apply counterparty credit risk concepts like Potential Future Exposure.
• Develop data pipelines on AWS (S3, Lambda, EMR, Glue).
• Follow software engineering practices (Git, CI/CD, tests).

Skills

Python

NumPy

pandas

SciPy

scikit-learn

QuantLib

SQL

Monte Carlo

Time series

Credit risk

AWS

ETL / Data pipelines

Git

CI/CD

Spark

NoSQL

Education

Bachelor's degree

Tools

S3

Lambda

Batch

Glue

EMR

CloudWatch

IAM

EC2

Spark

Hive

Airflow

Git

CI/CD

NoSQL

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 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