Role: Quantitative Analyst/Developer
Experience: 5 to 8 years (not a VP role)
Employment: W2 (USC and GC only)
Location: Jersey City, NJ
Work mode: Hybrid
Primary Requirement- Design, develop, and maintain research and prototype development platforms, including database architecture, stored procedures, query optimization, and performance tuning.
- Develop, test, deploy, and support quantitative model prototypes, analytical tools, and automated workflows; monitor daily scheduled jobs and production processes.
- Perform NSCC margin and stress testing model monitoring, performance reporting, and analysis to support risk management activities.
- Collaborate with quantitative researchers and risk teams to support model development, research initiatives, and data analysis needs.
- Translate business requirements into technical specifications and independently design, build, test, and document small-to-medium-scale projects.
- Serve as a liaison between Financial Engineering, Market Risk, Risk Technology, and application development teams, facilitating effective communication between business and technical stakeholders.
Qualification- Master's degree in a quantitative discipline and at least 5 years of experience in quantitative development, database development, or financial model implementation.
- Strong programming skills in Python, including experience with libraries such as NumPy, Pandas, SciPy, SQLAlchemy, pyodbc, subprocess, logging, and Snowpark.
- Advanced knowledge of SQL and relational databases, with hands-on experience in database design, stored procedure development, query optimization, and performance tuning; Snowflake experience strongly preferred.
- Proficiency with Linux, Git, Bitbucket, and modern software development practices.
- Experience developing, implementing, or supporting financial and quantitative models, with familiarity with financial engineering concepts and terminology.
- Strong analytical, communication, and problem-solving skills, with the ability to work independently, translate business needs into technical solutions, and effectively bridge communication between quantitative researchers and software developers.
