Quantitative Researcher - PM Monetisation

Job Application for Quantitative Researcher - PM Monetisation at Man Group London

About Man Group

Man Group is a global alternative investment management firm focused on pursuing out performance for sophisticated clients via our Systematic, Discretionary and Solutions offerings. Powered by talent and advanced technology, our single and multi-manager investment strategies are underpinned by deep research and span public and private markets, across all major asset classes, with a significant focus on alternatives. Man Group takes a partnership approach to working with clients, establishing deep connections and creating tailored solutions to meet their investment goals and those of the millions of retirees and savers they represent.

Headquartered in London, we manage $193.3 billion
* and operate across multiple offices globally. Man Group plc is listed on the London Stock Exchange under the ticker EMG.LN and is a constituent of the FTSE 250 Index. Further information can be found at

* As at 30 June 2025

Man AHL is one of the world’s longest running diversified systematic investment managers, trading in over 800 markets globally and offering a range of absolute return and long-only quantitative strategies that invest across traditional and alternative markets.

With over three decades of quantitative investment experience, Man AHL is committed to constant innovation and evolution of research. It applies advanced technology and scientific rigour to every stage of the investment process, from data curation and cleaning through to signal generation, risk management and execution. It views risk management and trading and execution as central to alpha generation, and its strategies are designed to understand risk, take appropriate exposures and, where necessary, dynamically adjust exposure.

Man AHL brings together scientists, academics, technologists and finance practitioners who are driven by curiosity, intellectual honesty and a passion for solving the complex problems presented by financial markets. It works closely with the Oxford-Man Institute of Quantitative Finance (OMI), Man Group’s unique collaboration with the University of Oxford, and leverages insights from its field‑leading academic research into machine learning and data analytics.

AHL Portfolio Management is the team responsible for the portfolio construction and investment management of the firm’s flagship fund. The team has been running for several years. It manages a diverse set of funds both in terms of trading styles and asset classes. It is also responsible for portfolio construction as well as allocation research inside AHL.

The Portfolio Management area is split into two sub teams:
Analytics and Monetisation. The Portfolio Analytics team’s purpose is to deliver quantifiable, transparent, and actionable insights into our research process.

The Portfolio Monetisation team’s purpose is to maximise the dollar output of our research in our funds.

Portfolio Monetisation:

As part of its mandate, the Portfolio Monetisation team is responsible for various research streams including alpha combination, alpha and fund allocations, portfolio construction and capacity evaluation. Additionally, the team is responsible for day‑to‑day portfolio management across AHL.

The ideal candidate will be a Quantitative Researcher with a strong background in portfolio construction with 2‑4 years relevant experience.

The candidate will be involved in several areas:

• Improvement of existing portfolios
• Cutting edge research in alpha combination and allocations
• Development of portfolio construction methods
• Day to day portfolio and risk management
• Contributing to AHL’s overall research effort by interacting with and collaborating with other research teams

Essential

• Exceptional analytical skills; recognised by your peers as an expert in your domain
• A deep understanding of statistics/machine learning/portfolio construction techniques
• Expertise in a high‑level programming language such as Python.
• Proficiency with Num Py/Sci Py/Pandas or similar
• Ease of handling large data sets
• Understanding risk management…