Senior Research Scientist
Founded in 1937, Mesirow Financial is a private, independent and majority employee-owned firm with approximately 500 employees headquartered in Chicago, with offices in New York, Boston, Miami, San Francisco, London and Hong Kong.
Our Currency Management Division has approximately $80 billion in assets under management and delivers innovative, customised solutions to suit client needs – ranging from passive to active currency management. Our active alpha models have a 14-year track-record of outstanding risk-adjusted performance. In the currency alpha group, we employ a diversified collection of systematic trading models that encompass momentum, mean-reversion, statistical pattern recognition and relative value trading.
We mix mathematics, computer science, statistics and engineering with large amounts of data to understand and predict the financial markets, in particular the foreign exchange markets. We are a research led organisation, utilising the latest technology to implement our trading and risk models for our clients and investors. We have a collaborative, scientific approach with a focus on robust peer review and rigorous in-sample and out-of-sample performance analysis to ensure the new models we develop add to the existing collection of models that have a long track record of consistent, non-correlated returns.
This individual will have an opportunity to grow and have an impact on what we do. Our culture is open, transparent and collaborative and we are actively engaged with the broader quantitative finance community through conferences and academic outreach. This individual will help build the trading models of the future by discovering and modelling predictable structure in vast collections of financial market and related data. This role will be based in our London office, with occasional travel required.
- Research and analysis of extensive and varied data sets from both global financial markets and non-financial market sources.
- Developing robust forecasting models using a portfolio of machine learning and other signal-processing, statistical and econometric techniques
- Applying these in the development and implementation of new trading, execution, risk-management and portfolio construction applications.
- Taking projects from initial idea generation through to implementation and execution, tackling challenges in areas such as prediction, optimization and data analysis.
- PhD or exceptional Masters qualification in a quantitative subject.
- Strong programming and scientific computing skills in Python. Ability to write clear, well documented and scalable code.
- Knowledge of standard python libraries and of Pandas, NumPy, StatsModels, TensorFlow, SciKit-Learn. Experience in Linux python IDE (e.g. Sublime Text) and interactive development (e.g. Jupyter).
- Experience of prototyping languages (e.g. R, MatLab, Mathematica) to explore and develop new ideas a plus.
- Demonstrable experience in some of the following areas: numerical analysis, optimisation, signal processing, statistics (including extreme value theory), time series analysis, machine learning, natural language processing. Ability to select the appropriate techniques to use in different circumstances.
- Practical hands-on experience in one or more deep-learning technologies such as convolutional neural networks, Recurrent Neural Networks, Reinforcement Learning. Viewable code in a private or public code repository (e.g. github, bitbucket) or contribution to open source machine learning projects a plus.
- Excellent communication skills and enjoyment of collaborating to solve practical problems.
- A passion for understanding financial markets through empirical data analysis and modelling
- Experience of developing in a Linux environment and of deployment to a distributed Linux server environment.
- Someone who is driven by curiosity and intellectual honesty with a passion for solving the complex problems presented by financial markets.
- Experience of working in a shared, collaborative code base using standard version control tools (git or subversion) and standard issue tracking tools (e.g. Jira).
- Experience of relational databases (MS SQL Server, MySQL), including writing SQL queries and reading/writing data using python.