Senior Quantitative Researcher: MFE

Maven’s Systematic Equities team deploys research-driven strategies across global equity markets, powered by some of the most advanced technology available. Our approach to trading is scientific and process-driven, with a strong emphasis on a flexible research environment, providing us with efficient means to rapidly develop, test, and deploy new ideas. We empower our team members and maximise their ability to succeed by offering an environment that is open, collaborative, and supportive. We value creativity and are aggressive to capitalise on opportunities. The role: We are seeking a Senior Quantitative Researcher to join a systematic equities investment team. The role focuses on the research, development, and implementation of quantitative equity strategies across global developed markets. The successful candidate will contribute across the full investment research lifecycle, including data acquisition, alpha signal research, portfolio construction, backtesting, production implementation, and ongoing strategy monitoring. Job Description Conduct quantitative research to develop alpha signals for systematic equity strategies across global markets, including developed and emerging markets. Research and implement cross-sectional equity signals using traditional financial data, alternative datasets, and advanced statistical or machine learning techniques. Design, test, and improve idiosyncratic, factor-neutral and long/short systematic strategies. Evaluate alpha signals and strategies through rigorous backtesting, performance attribution, robustness analysis, and out-of-sample testing. Apply machine learning methods such as tree-based models, graphical models, NLP, explainable AI, dimensionality reduction, and network-based approaches to investment research. Collaborate with portfolio managers, researchers, data engineers, and technology teams to translate research ideas into production-ready investment strategies. Contribute to the development of internal research tools, analytics platforms, and model evaluation frameworks. Stay current with academic and industry developments in quantitative finance, machine learning, alternative data, and systematic investing. Required Qualifications Advanced degree (Masters or Ph.D.) in a quantitative discipline such as mathematics, statistics, computer science, engineering, physics, economics, financial engineering. 8+ years experience in quantitative research, systematic equities, asset management, hedge funds, proprietary trading, or a related investment environment Strong understanding of equity markets, factor investing, alpha research, portfolio construction, and risk management Strong programming skills, preferably in Python, with experience working with large financial datasets and research infrastructure. Solid knowledge of statistics, econometrics, machine learning, optimization, and time-series or cross-sectional modelling. Ability to work independently across the full research lifecycle, from idea generation to live deployment. Strong communication skills, with the ability to explain complex quantitative concepts to both technical and non-technical stakeholders. Academic teaching, publishing, or thought leadership in machine learning, quantitative finance, or systematic investing is a plus.