Quantitative Python Developer Risk & Data Engineering (AWS/SQL)

Overview

On Site
Hybrid
Up to $70
Full Time

Skills

Python
quantitative model development
Potential Future Exposure
Amazon Web Services
Amazon S3
Amazon EC2
Finance
SQL
Software Engineering
Statistics
Data Engineering
Credit Risk
Continuous Integration
Continuous Delivery
Cloud Computing
Analytics
Big Data
Data Manipulation
Data Modeling
Database
Derivatives
Extract
Transform
Load

Job Details

We are seeking a highly skilled Quantitative Python Developer with strong expertise in data engineering, risk modeling, and AWS cloud services. The ideal candidate will combine deep quantitative modeling experience with advanced software engineering skills to build, test, and deploy scalable solutions for mortgage and counterparty credit risk analytics. This role requires hands-on coding, data manipulation, model implementation, and integration with large-scale financial datasets.

Key Responsibilities:

  • Develop, optimize, and maintain Monte Carlo simulations, time-series models, and risk exposure models in Python.

  • Utilize NumPy, pandas, SciPy, statsmodels, scikit-learn, and QuantLib for statistical analysis and quantitative modeling.

  • Work with large-scale mortgage and loan datasets using advanced SQL techniques.

  • Build, test, and maintain production-ready Python/Shell code with Git, unit testing, and CI/CD best practices.

  • Design and optimize datasets, normalize databases, and ensure structures meet application and reporting requirements.

  • Integrate and manage large-scale data pipelines using Spark, Hive, and Airflow in a cloud environment.

  • Deploy and manage solutions using AWS services (S3, Lambda, Batch, Glue, EMR, EC2, CloudWatch, IAM).

  • Combine raw data from multiple sources into consistent, machine-readable formats for analytics and reporting.

  • Collaborate with quantitative modelers, risk teams, and business stakeholders to translate financial requirements into technical solutions.

  • Clearly communicate technical designs, assumptions, and results to both technical and non-technical audiences.

Required Skills & Qualifications:

  • Proficiency in Python with quantitative/statistical libraries (NumPy, pandas, SciPy, statsmodels, scikit-learn, QuantLib).

  • Strong SQL skills and experience with relational databases and large datasets.

  • Experience designing and implementing Monte Carlo simulations and time-series models.

  • Hands-on experience with AWS services (S3, Lambda, Batch, Glue, EMR, EC2, IAM, CloudWatch).

  • Experience with Git, CI/CD pipelines, unit testing, and production software engineering practices.

  • Familiarity with data modeling, ETL pipelines, and big data tools (Spark, Hive, Airflow).

Preferred Skills:

  • Familiarity with Potential Future Exposure (PFE) methodologies for counterparty credit risk.

  • Understanding of interest rate modeling using time-series techniques.

  • Basic knowledge of derivative pricing and exposure dynamics.

  • Exposure to macro risk factor models relevant to mortgage portfolios.

Education:

  • Master s degree in Data Science, Computer Science, Applied Mathematics, Financial Engineering, or related field.

  • Bachelor s degree with 5+ years of quantitative model development experience will also be considered.

Soft Skills:

  • Strong analytical and problem-solving skills with high attention to detail.

  • Ability to explain complex models and technical concepts to non-technical stakeholders.

  • Strong collaboration and communication skills.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.