Quantitative Research Analyst, Investment Risk and Capital Markets Research (Grantham, Mayo, van Otterloo & Co. LLC)

Company Profile   Founded in 1977, GMO is a global investment manager committed to delivering superior long-term investment performance and advice to our clients. We offer investment strategies and solutions where we believe we are positioned to add the greatest value for our investors. These include multi-asset class, equity, fixed income and alternative offerings.   We manage approximately $80bn for a client base that includes many of the world’s most sophisticated institutions, financial intermediaries, and private clients. Industry-wide, we are well known for our focus on valuation-based investing, willingness to take bold positions when conditions warrant, and candid and academically rigorous thought leadership. Jeremy Grantham, GMO’s Co-Founder and Long-Term Investment Strategist, is renowned as an expert in identifying speculative investment bubbles and also as a leading climate investor and advocate.   GMO is privately owned and employs over 430 people worldwide. We are headquartered in Boston, with additional offices in Europe, Asia and Australia. Our company-wide culture emphasizes commitment to clients, intellectual curiosity, and open debate. We celebrate and respect our differences, while embracing and valuing what each of us brings to work, as we know that diverse teams in an inclusive, caring environment achieve higher engagement and better client results.   Please follow the prompts included in this job posting to apply. The application window for this role is anticipated to remain open until the job is filled, or as otherwise determined by GMO.

Using deep understanding of GMO’s forecast models, contribute to monthly model reviews and assist with the implementation of infrastructure improvements to streamline processes, enhance efficiency, and support more accurate and timely updates to investment forecasts; Conduct research projects to enhance GMO forecast components, including refining methodologies and the investment universe, integrating new data sources, and adjusting model parameters to improve the accuracy, reliability, and predictability of our forecasting framework; Update and maintain forecast-related client materials and analytics across the asset allocation landscape, including equities, fixed income, credit, and FX markets. Provide actionable quantitative insights that address client inquiries regarding asset class views and forecasts to assist Asset Allocation strategists and the GCR team; Migrate portfolio analytics tool across fund strategies to a user-interactive Python-based dashboard, improving the accessibility of key analytics for multiple functions within the investment teams; Guide the GMO Asset Class Forecast and Capital Market Assumptions monthly update process to recommend investment decisions to the Asset Allocation team and facilitate strategic, solutions-oriented conversations with clients. Co-host Quarterly Forecast Review forums to discuss the attractiveness of asset classes and provide insights into the key drivers influencing asset class returns; Transition forecast models to a new Python-based code platform, enabling downstream applications for forecast analytics and improving the accessibility of key forecast-related insights for strategists, portfolio managers, and clients; Conduct research on topics that can be leveraged to enhance investment risk management strategies and improve the risk monitoring process in partnership with the Investment Risk team. May telecommute part of the week from commuting distance to Boston, MA in accordance with the company’s flexible working policy. Minimum Requirements:  A Master’s degree (or foreign equivalent) in Mathematics, Economics, Finance, Statistics, or a related quantitative discipline plus 3 years of experience in a quantitative research analyst- or financial analyst-related occupation. Experience must include the following, which may have been gained concurrently: 1) 3 years of experience developing, maintaining,