Data Modeling
Data Modeling
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1 Vacancy
Job Description
Skill
Data Modeling
Location
Reston VA
Working Model
Onsite 5 days a week
Education/Experience
Master s in Data Science Computer Science Applied Math or Financial Engineering; or Bachelor s in same fields with 5 years of quantitative model development experience in Python SQL.
Technical Skills
Proficiency in Python with strong experience using quantitative/statistical packages (NumPy pandas SciPy statsmodels scikit-learn QuantLib).
Strong SQL skills for working with large mortgage/loan datasets.
Ability to design implement and optimize Monte Carlo simulations and time-series models.
Experience building testing and maintaining production-ready Python/Shell code with Git unit testing and CI/CD.
Hands on experience with AWS services like Amazon S3 AWS Lambda AWS Batch AWS Glue AWS EMR Cloudwatch and IAM EC2
Quantitative Modeling Knowledge
Familiarity with Potential Future Exposure (PFE) methodologies for counterparty credit risk.
Understanding of interest rate modeling using time series techniques.
Basic understanding of derivative pricing and exposure dynamics.
Exposure to macro risk factor models relevant to mortgage portfolios.
Soft Skills
Strong analytical and problem-solving skills with attention to detail.
Ability to clearly communicate results and technical design to both modelers and business stakeholders.
Focused on manipulating data in a software engineering capacity. Some of that data might live in relational systems but its increasingly moving towards NoSQL systems and data lakes. Normalize databases and ascertain the structure of the data meets the requirements of the applications that are accessing the information. Construct datasets that are easy to analyze and support company requirements. Combine raw information from different sources to create consistent and machine-readable formats. Skills: This IT role requires a significant set of technical skills including a deep knowledge of SQL data modeling and tools like Spark/Hive/Airflow
Employment Type
Full-time
