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Quantitative Analyst - Jersey City, NJ (Not a VP role)

StradITJersey City, New Jersey🇺🇸United States

Why this role stands out

This hybrid Quantitative Analyst role offers a fantastic opportunity to build and refine cutting-edge financial models, directly impacting risk management and research initiatives within a reputable firm. If you possess strong Python and SQL skills, thrive on technical challenges, and enjoy collaborating with diverse teams, you'll find significant growth and impact here. Apply today to leverage your expertise and advance your career in a dynamic environment.

Quick overview

SeniorityLeader

Work modeHybrid

LocationJersey City, New Jersey, United States

SQLNumPySciPySnowflakeGitPandasPythonRisk ManagementData AnalysisLinux

Job description

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.