Lead Quantitative Analyst: Build Scalable Investments

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

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.