Quantitative Analyst

Confidential; New York (NY); On-site; USD 180,000 - 340,000; Full time

Job summary

Confidential aims to recruit a senior quantitative engineer to design and maintain research infrastructure enabling scalable development of investment strategies. You will build high-performance back-testing, data pipelines, and analytics tools to evaluate hypotheses and portfolio construction.

You will collaborate with Quant and Technology teams, apply ML tools, and enforce production-grade engineering principles to ensure reliability and performance across systems.

Qualifications

• Advanced degree in a quantitative field (CS/Engineering/Applied Math/Physics).
• 2–15 years of production front-office quant or investment team experience.
• Expert Python with quantitative research frameworks and data pipelines.
• Strong understanding of time series, tick data, and corporate actions.
• Experience with signal research, alpha modeling, and back-testing.

Responsibilities

• Design, implement, and maintain research infrastructure for systematic and discretionary strategies.
• Develop high-performance back-testing and simulation frameworks.
• Engineer data pipelines to manage market, factor, and alternative datasets.
• Collaborate with Technology and Quant Engineering to align infrastructure with standards.
• Build visualization tools for real-time portfolio metrics and risk attribution.
• Enhance portfolio construction frameworks for systematic and hybrid approaches.
• Advocate engineering best practices, including testing, version control, and CI/CD.

Skills

Python

Data pipelines

Back-testing

ML tools

Communication

Collaboration

Education

MS/PhD in CS/Engineering/Applied Math/Physics

Tools

Git

CI/CD

Testing frameworks

NumPy

Pandas

scikit-learn

PyTorch/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.