Quantitative Researcher

Millennium•New York, NY

1d•$160,000 - $250,000

About The Position

Millennium's Global Risk Management Department is responsible for identifying, measuring, monitoring, managing and reporting on the risks associated with Millennium's portfolios at the Firm-wide and Portfolio Manager levels, with a focus on the market, credit and liquidity risks the Firm faces in the course of its business. At the Portfolio Manager level, the team establishes and monitors trading parameters, risk guidelines and performance metrics across three regions and multiple product classes, and also monitors aggregations of risk up to the full Firm-wide level. Within Risk Management, the Quantitative Strategies team is a collaborative and entrepreneurial investment team that develops quantitative investment strategies across asset classes, including equities, fixed income, commodities, credit and FX, and across a broad set of instruments spanning futures, forwards, options, swaps and cash products. The team works across the full research lifecycle: idea generation, data sourcing, signal development, model implementation, backtesting, portfolio construction and live strategy refinement.

Requirements

Advanced degree in an applied quantitative field such as statistics, econometrics, computer science, engineering, operations research, financial engineering, applied mathematics, or data science; PhD preferred but not required, and exceptional candidates with a Bachelor’s or Master’s degree or equivalent industry experience will also be considered
Excellent Python skills, including experience with common scientific/data libraries such as pandas, NumPy, SciPy, Polars, scikit-learn, or similar tools, and the ability to build clean, scalable research code.
Strong grounding in statistics, probability, optimization, and empirical modeling, with rigorous model evaluation and healthy skepticism around overfitting.
Experience working with large financial datasets, market data, and reproducible research workflows.
Ability to build clean, scalable research code and evaluate models rigorously.
Experience in quantitative research, systematic investing, hedge funds, asset management, or related research environments, with a preference for training rooted in applied problem-solving rather than purely theoretical work.
Strong preference for experience in QIS (Quantitative Investment Strategies), systematic equities, cross-asset, or multi-asset derivatives research, especially tail hedging strategies; experience researching or trading systematic equities, credit, or volatility is highly desirable.
Familiarity with derivatives and implementation considerations across options, swaps, and forwards; experience with machine learning, trading cost analysis, or intraday strategy research is helpful but not required.
Intellectual curiosity and genuine interest in markets and alpha research, creativity and proactive problem-solving, and the demonstrated ability to conduct independent research, communicate results clearly, and work independently in a transparent, collaborative team.

Nice To Haves

PhD preferred but not required, and exceptional candidates with a Bachelor’s or Master’s degree or equivalent industry experience will also be considered
experience with machine learning, trading cost analysis, or intraday strategy research is helpful but not required.

Responsibilities

Conduct original research and develop systematic investment strategies across all asset classes, including equities, rates, commodities, credit, and FX.
Generate and test new research ideas using financial intuition, statistical learning, and large, diverse datasets.
Build and improve research infrastructure, including data pipelines, signal analytics, backtesting tools, and portfolio analytics.
Analyze strategy performance with attention to robustness, implementation, transaction costs, liquidity, and risk exposures.
Research opportunities across futures, forwards, options, swaps, and cash instruments, including relative value, directional, and cross-asset themes.
Partner closely with portfolio managers, researchers, and technologists to move ideas from research into production.
Monitor live strategies and refine models based on empirical results and changing market behavior.