Quantitative Analyst, Quant Analyst Program

About the Role

Millennium is launching a Quant Analyst Program designed to develop early-career quants over 18–24 months into strong, cross-asset systematic researchers. This program involves formal technical training, asset class rotations, cross-functional exposure, and supervised contribution to the quantitative strategies platform, with senior quant mentorship and quarterly development goals.

What You’ll Do

• Complete a structured 18–24 month development program combining formal technical training in backtesting methodology, signal design, portfolio construction, risk and volatility modeling, and transaction cost modeling.
• Rotate through asset class research across equities, FX, rates, commodities, and options/volatility, learning market structure, common alpha sources, implementation constraints, and risk frameworks in each.
• Conduct original research and contribute new signals and features across quant themes, including equity risk premia, carry, momentum, value, flows, volatility, and dispersion.
• Build and improve shared research infrastructure, including data pipelines, backtesting tools, risk and volatility models, transaction cost models, and performance diagnostics.
• Analyze strategy performance with attention to robustness, implementation, transaction costs, liquidity, and risk exposures, following the team's standards for reproducible, peer-reviewed research.
• Gain hands-on exposure to trading, operations, compliance, technology, and risk to understand how ideas become investable strategies.
• Partner closely with senior quants, portfolio managers, and technologists to move ideas from research into shadow and production.
• Present research in regular team seminars and take ownership of an independent research project, with scope increasing over the program.

Requirements & Tech Stack

• Strong Python skills, including experience with common scientific/data libraries such as pandas, NumPy, SciPy, Polars, scikit-learn, or similar tools.
• SQL experience and C++ are a plus.
• Strong grounding in statistics, probability, optimization, and empirical modeling, with rigorous model evaluation and healthy skepticism around overfitting.
• Genuine interest in financial markets and systematic investing.
• Research discipline and the demonstrated ability to conduct independent, reproducible research, evidenced by academic projects, a thesis or dissertation, publications, or internships.
• Familiarity with derivatives, machine learning, large datasets, or time series analysis is helpful but not required.
• High intellectual curiosity, creativity, and proactive problem-solving.
• Strong written and verbal communication skills and the ability to work in a transparent, collaborative team.

Qualifications

• Advanced undergraduate, Master’s, or PhD degree (completed or expected) in a quantitative field such as mathematics, statistics, computer science, physics, engineering, or economics/econometrics.