Quantitative Developer

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 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 $65bn 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. Department Profile GMO’s Investment Data Solutions (IDS) Team consists of over 35 technology and data engineering professionals who partner closely with GMO’s investment managers and research teams to provide structured and unstructured market data, data engineering and analysis, quantitative application development, operations, and support in all areas of the research and investment process. Utilizing cloud-based tools and architectures, our work spans fundamental and alternative data pipeline creation, data engineering, portfolio construction, optimization, investment analytics and more. The team prides itself on an open culture of sharing, learning, and applying new technology approaches as well as problem solving, open debate and comradery. We are a focused team of data and technology professionals who work in an agile framework to deliver timely and on-demand solutions and frameworks. Position Overview We are seeking a Quantitative Developer to partner with multiple systematic investing teams. You will work on a variety of projects focused on different aspects of the investment process, including data loading, research tools, and model analytics. You will help build new data processing and quantitative tools using Python and a cloud-native, state of the art scalable-computing platform. You will work closely with Researchers and Portfolio Managers and focus on all aspects of the research and production code that support the team’s investment processes from model building to portfolio construction. You will develop a thorough quantitative and economic understanding of the models, and a comprehension of what inputs drive the investment process. Responsibilities:

Quant Development: Support existing research platform and strategy/portfolio applications by developing, enhancing, testing, and deploying production model code. Quant Operations: Work closely with researchers and investment professionals to provide operational support for running quant models. Platform Migration: Assist in migrating code to a new Python-based quantitative research platform. Suggest modern architectures by partnering with other Technology Team members to ascertain the best end-to-end solution. Software Engineering: Utilize industry standard best practices for software design and implementation, lead internal code review processes, provide code analysis, and proactively identify software risks. Data Pipeline Management: Design and develop efficient end-to-end data and analytical solutions that support internal business requirements, using a Python stack. Team Participation: Actively participate in GMO Python/new platform working groups and engage in agile/scrum activities. Requirements:

Bachelor’s or equivalent college degree required Advanced degree in computer-science, data-science, engineering, math, or science preferred Familiarity with statistics and experience working with optimization libraries (open-source optimization libraries like cvxpy, commercial solvers like Gurobi) is helpful Matlab experience a plus, not required A minimum of 3-5 years of experience in Python, including package development Solid understanding and application of software design principles Experience with SQL queries and database development using relational databases is preferred Experience with git is strongly preferred Experience and understanding of modern CI/CD DevOps and orchestration tools such as Azure DevOps, Airflow, Kubernetes and Docker is a bonus Experience using cloud-based large data platforms such as Databricks, Synapse, Data Lakehouse is a plus too

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