Department ProfileGMO'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 OverviewWe 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 requiredAdvanced degree in computer-science, data-science, engineering, math, or science preferredFamiliarity with statistics and experience working with optimization libraries (open-source optimization libraries like cvxpy, commercial solvers like Gurobi) is helpfulMatlab experience a plus, not requiredA minimum of 3-5 years of experience in Python, including package developmentSolid understanding and application of software design principlesExperience with SQL queries and database development using relational databases is preferredExperience with git is strongly preferredExperience and understanding of modern CI/CD DevOps and orchestration tools such as Azure DevOps, Airflow, Kubernetes and Docker is a bonusExperience using cloud-based large data platforms such as Databricks, Synapse, Data Lakehouse is a plus too
