Quantitative Developer

Position: Quantitative Developer Location: Reston, VA (Hybrid) Duration: 03+ Months (Highly likely to extend till next year) Job Description: Job Overview Seeking a highly skilled Quantitative Data Scientist / Application Developer. This role will focus on building, optimizing, and maintaining data-driven solutions that support quantitative modeling, risk analysis, and large-scale data processing. The ideal candidate will combine strong technical expertise in Python, SQL, and AWS with applied quantitative modeling knowledge to design robust datasets, simulations, and analytics pipelines that drive critical business decisions. Responsibilities • Design, implement, and optimize quantitative models, including Monte Carlo simulations and time-series models. • Build, test, and maintain production-ready Python/Shell code with Git, unit testing, and CI/CD pipelines. • Develop and manage large-scale datasets across relational, NoSQL, and data lake environments to ensure accessibility, normalization, and performance. • Combine raw data from multiple sources into machine-readable, consistent formats to support modeling and analytics. • Work with Spark, Hive, and Airflow for large-scale data processing and pipeline automation. • Apply quantitative techniques to support risk management, exposure modeling, and mortgage/loan portfolio analysis. • Partner with modelers and business stakeholders to clearly communicate technical designs, results, and insights. • Utilize AWS services (S3, Lambda, Batch, Glue, EMR, CloudWatch, IAM, EC2) to manage cloud-based data and model workflows. Required Qualifications: Education/Experience: • Master’s in Data Science, Computer Science, Applied Math, or Financial Engineering; • OR Bachelor’s in the same fields with 5+ years of hands-on quantitative model development experience. Technical Skills: • Strong proficiency in Python with expertise in packages such as NumPy, pandas, SciPy, statsmodels, scikit-learn, and QuantLib. • Advanced SQL skills for managing large mortgage/loan datasets. • Proven experience with version control (Git), unit testing, and CI/CD. • Hands-on experience with AWS services (S3, Lambda, Batch, Glue, EMR, CloudWatch, IAM, EC2). • Familiarity with Spark, Hive, and Airflow for large-scale data processing. Quantitative Knowledge: • Understanding of Potential Future Exposure (PFE) methodologies for counterparty credit risk. • Familiarity with interest rate modeling using time-series methods. • Basic knowledge 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 exceptional attention to detail. • Ability to explain technical designs and modeling results to both technical and non-technical stakeholders. “Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of – Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.”