Quantitative Analyst

Confidential; New York (NY); On-site; USD 180,000 - 320,000; Full time

Job summary

Confidential in New York seeks a senior quantitative engineer to drive research and build scalable, production-grade investment infrastructure. You will craft Python-based research, data pipelines, and backtesting frameworks that evaluate hypotheses and optimize portfolios.

You will collaborate with Technology and Quant Engineering to align standards, deliver robust analytics, and maintain high-quality, testable code.

Qualifications

• Advanced degree in a quantitative field (MS/PhD) required.
• 2–15 years of production experience in a front-office quant or investment team.
• Proven Python and data-pipeline development experience.

Responsibilities

• Design, implement, and maintain core research enabling scalable development of systematic and discretionary strategies.
• Develop high-performance back-testing and simulation frameworks to evaluate investment hypotheses, strategy performance, and portfolio construction approaches.
• Engineer robust data pipelines to integrate, clean, and manage market, factor, and alternative datasets.
• Partner with Technology and central Quant Engineering to align infrastructure with firmwide standards and shared systems.
• Build visualization and analytics tools to present real-time portfolio metrics, risk exposures, and performance attribution.
• Enhance portfolio construction and optimization frameworks for systematic and hybrid approaches.
• Champion engineering best practices: modular architecture, rigorous testing, version control, and CI/CD—production-grade infra.

Skills

Python programming

Quantitative research

Data pipelines

Backtesting

Portfolio optimization

Git

CI/CD

ML frameworks

NumPy

Pandas

scikit-learn

PyTorch

TensorFlow

Communication

Education

MS or PhD in Computer Science, Engineering, Applied Mathematics, Physics, or related quantitative field

Tools

Git

CI/CD

Jupyter

SQL

PySpark

TensorFlow

Job description

• Design, implement, and maintain the team’s core research, enabling scalable development of systematic and discretionary strategies.
• Develop high-performance back-testing and simulation frameworks to evaluate investment hypotheses, strategy performance, and portfolio construction approaches.
• Engineer robust data pipelines to integrate, clean, and manage market, factor, and alternative datasets
• Partner with Technology and central Quant Engineering to align investment team infrastructure with firmwide standards and shared systems.
• Build visualization and analytics tools to present real-time portfolio metrics, risk exposures, and performance attribution in intuitive, interactive formats.
• Enhance portfolio construction and optimization frameworks, supporting both systematic and hybrid investment approaches.
• Champion engineering best practices, including modular architecture, rigorous testing, version control, and continuous integration — ensuring infrastructure is reliable, maintainable, and production-grade.

Requirements

• Advanced degree (MS or PhD) in Computer Science, Engineering, Applied Mathematics, Physics, or a related quantitative field.
• 2-15 years experience implementing code in production platforms within a front-office quant or investment team environment.
• Expert-level proficiency in Python, with demonstrated experience building quantitative research frameworks, data pipelines, and performance-sensitive analytics.
• Strong understanding of investment data structures, including time series, tick-level, and corporate action data
• Experience in signal research, alpha modeling, and back‑testing, with practical understanding of portfolio optimization and risk modelling.
• Deep familiarity with data science and ML tools (NumPy, Pandas, scikit‑learn, PyTorch/TensorFlow) and software engineering practices (Git, CI/CD, testing frameworks).
• Excellent communication and collaboration skills, capable of operating within a flat, fast-paced investment environment.
• High attention to detail and a commitment to code quality, reliability, and production-readiness.
• Proactive, and delivery-oriented, with a passion for building systems that directly drive investment performance.