Research and Investment Data Analyst

Company Profile
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 $70 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. 
Position Overview
We are looking for a Research and Investment Data Analyst to join a high performing team focused on building and maintaining research databases used in a state-of-the-art quantitative investment process.  This person will act as the first responder for data quality monitoring and resolution of all daily and monthly processing. They will monitor, develop, and enhance data quality controls and processes across the platform. This position offers a unique opportunity to learn about all facets of data used in the investment process. An ideal candidate should have a working knowledge of financial data used in a multi-asset class quantitative investment process and have a strong desire to think about and solve data quality issues and be adept at using various tools and technologies at their disposal to execute the job.

Key Responsibilities

• Monitor incoming data to detect anomalies and remediate issues.
• Investigate and resolve data quality questions and concerns.
• Develop an expert understanding of internal databases and processes and ensure information is maintained accurately and efficiently.
• Become a “subject matter expert” in a variety of data categories (reference, market, fundamental, estimate, macro-economic, et al) across various asset classes including equity and fixed income.
• Develop a keen understanding of how data is utilized in our investment processes to help design and implement new data sets and processes.
• Partner with investment teams and respond to and proactively address their data needs.
• Design and build efficient and effective controls to help resolve and identify potential data issues.
• Coordinate with engineering team to identify ways to improve our data and monitoring processes.
• Work with external market data vendors to bring in any new data sources required by the business users and support existing feeds.

Required

• Bachelor’s degree, preferably in economics, statistics, computer science or math
• Attention to detail
• Experience with MS SQL
• Experience with Python
• Experience with financial data
• Experience working with large data sets
• Passionate about data, data quality and robust quantitative analysis
• Strong communication skills
• High energy and positive attitude – candidate should be conscientious, self-directed and possess a strong work ethic.
• Strong interest in databases and database technologies

Plus

• Experience with data systems implemented in the cloud using Delta Lake, Spark, and Hadoop
• Experience with Data Governance toolsets
• Experience with Visualization Tools such as PowerBI, Qlik, Tableau
• Interest in, or experience with, Machine Learning for Anomaly Detection