Founded in 1977, GMO is a private partnership committed to delivering superior investment performance and advice to our clients. We offer strategies where we believe we are positioned to add the greatest value for our investors. These include multi-asset class portfolios as well as dedicated equity, fixed income, and absolute return offerings, many of which employ the firm’s proprietary 7-year asset class forecasting framework. Our client base is comprised primarily of institutions, including corporate and public defined benefit and defined contribution retirement plans, endowments, foundations, and financial intermediaries.
GMO, whose sole business is investment management, employs approximately 470 people worldwide and is headquartered in Boston with offices in San Francisco, London, Amsterdam, Sydney, and Singapore. We manage roughly $65 billion in client assets using a combination of top-down and bottom-up approaches that blend traditional fundamental insights with innovative quantitative methods to identify undervalued asset classes and securities. Our valuation-based approach embeds several key factors, including: a long-term investment horizon, discipline, conviction, and a commitment to research. Our research emphasizes not only identifying and exploiting pricing dislocations but also understanding the long-term drivers of return in the markets in which we invest. We are known for our candor in sharing our views with clients and for our willingness to take bold, differentiated positions when opportunities warrant.
The Investment Data Solutions (IDS) Team provides investment data, data engineering & science, quant & application development, operations and support to GMO’s investment teams in all areas of the investment process. Our work spans fundamental, market & alternative data, data warehousing on-premises & in the cloud, data quality, portfolio construction & optimization, investment analytics and more.
The team prides itself on an open culture of sharing and learning new technologies, problem solving, and comradery. We are a focused team of data & technology professionals who work in an agile framework to deliver timely and on-demand solutions using the latest cutting-edge software and methodologies.
This team consists of approximately 30 technology professionals who collaborate with all investment teams in GMO (including equity, fixed income and asset allocation teams), Performance Analytics and Business Development.
This position will predominately partner with the Asset Allocation team which is responsible for managing multi-asset portfolios and conducting research across a variety of asset classes. The team values their culture of intellectual curiosity, debate, respectful disagreement, candor, and collegiality.
We are seeking a Quant Developer to work as an embedded resource with the Asset Allocation team. You will work on a variety of projects focused on different aspects of the asset class forecasting process, from data loading to model generation to analytics. You will work closely with Asset Allocation Researchers and focus on owning the Asset Allocation production model & forecasting code. During this you will develop a thorough quantitative and economic understanding of the models and forecasts, and a comprehension of what inputs drive the model outputs.
- Design and develop complex software applications supporting internal business requirements using MATLAB and Python stack.
- Support existing applications: support, develop and deploy fixes.
- Create new system components, enhance existing components.
- Ensure that implementation adheres to GMO’s architecture best practices and coding standards.
- Participate in code reviews, code analysis, and identification of software risks.
- Participate in agile/scrum activities.
- Migrate the code to new quant infrastructure.
- Ensure that daily and monthly process run as expected. Refactor code and processes for optimal execution.
- Manage relationships with data and infrastructure counterparts in other investment teams to ensure smooth data and analytics integration with Asset Allocation.
- Gain expertise on model dependencies regarding market data and other internally sourced data sets.
- Create analytics for monitoring and improving efficacy of the process.
- Bachelor’s or equivalent college degree required
- Advanced degree in computer science, engineering, math, or science preferred
- A minimum of 5 years of professional experience in modern quantitative computing languages such as MATLAB, Python and/or R required
- Solid understanding and application of Object-Oriented Design principles
- Experience with SQL queries and database development using relational databases is preferred
- Experience and understanding of modern CI/CD DevOps practices and tools is preferred
- Working knowledge of SVN and GIT is strongly preferred
- Prior experience working with multiple assets classes or experience working directly with an investment team is preferred