Quantitative Researcher

Job Description

Quantitative Researcher | Millennium

The Tone:
This is a full-time role at Millennium, located in New York, NY. Millennium is a global, diversified alternative investment firm focused on delivering results for investors. The Global Risk Management Department, specifically the Quantitative Strategies team, develops systematic investment strategies across various asset classes and instruments. This role is essential for advancing the firm’s quantitative research capabilities, generating new investment ideas, and building robust infrastructure to support innovative trading strategies.

The TL;DR
• Role: Full Time
• Type: Full-time
• Location: In-person, New York, NY
• Pay: $160000–$250000 yearly
• Team: Quantitative Strategies team within the Global Risk Management Department.
• Mission: Conduct original research and develop systematic investment strategies to deliver results for investors through disciplined risk management.
• Tech Stack: Python, pandas, NumPy, SciPy, Polars, scikit-learn

What You’ll Actually Do
• Research & Strategy Development: Conduct original research and develop systematic investment strategies across equities, rates, commodities, credit, and FX.
• Idea Generation & Testing: Generate and rigorously test new research ideas utilizing financial intuition, statistical learning, and large, diverse datasets.
• Infrastructure Enhancement: Build and improve essential research infrastructure, including data pipelines, signal analytics, backtesting tools, and portfolio analytics.
• Performance & Risk Analysis: Analyze strategy performance with attention to robustness, implementation, transaction costs, liquidity, and overall risk exposures.
• Collaborative Implementation: Partner with portfolio managers, researchers, and technologists to move research ideas into production and refine live strategies based on empirical results.

The Must-Haves
• Background: An advanced degree in an applied quantitative field such as statistics, econometrics, computer science, engineering, operations research, financial engineering, applied mathematics, or data science. A PhD is preferred, though exceptional candidates with a Bachelor’s or Master’s degree and relevant industry experience will be considered.
• Experience: Professional experience in quantitative research, systematic investing, hedge funds, asset management, or related research environments, with a strong preference for applied problem-solving over purely theoretical work. Highly desirable experience includes QIS, systematic equities, cross-asset, or multi-asset derivatives research, especially tail hedging strategies, or researching/trading systematic equities, credit, or volatility.
• Skills: Excellent Python proficiency, including expertise with scientific/data libraries like pandas, NumPy, SciPy, Polars, or scikit-learn, enabling the construction of clean, scalable research code. Strong foundational knowledge in statistics, probability, optimization, and empirical modeling, paired with rigorous model evaluation and healthy skepticism towards overfitting. Ability to work with large financial datasets, market data, and maintain reproducible research workflows.
• Bonus: Familiarity with derivatives and their implementation considerations across options, swaps, and forwards. Experience with machine learning, trading cost analysis, or intraday strategy research. Intellectual curiosity, creativity, proactive problem-solving abilities, and a demonstrated capacity for independent research, clear communication of results, and collaborative teamwork.