Data Science Engineer

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. 
Department Profile 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. 

Responsibilities:

• Take a hands-on role as we partner with investment teams to advance their research agendas as requested: data ingestion, data cleansing, hypothesis generation, feature extraction and engineering, model development, training, and validation.  
• Develop data processing pipelines primarily in Python and Apache Spark, within the Azure Databricks environment.   
• Design and implement data collection processes from traditional and alternative data sources including but not limited to internal databases and APIs, third party data vendors, public web sites, government and industry regulatory bodies, and real time data streams. 
• Support existing production models & processes, and data quality checks.  
• Integrate model outputs and generated data products with GMO’s existing data, and within our analytics ecosystem. 
• Support and advance our data quality and governance initiatives including data lineage, metadata management, and dependency tracking.   
• Design and implement interfaces to visualize key data elements, model outputs, and operational details. 
• Support and advance our CI/CD practices leveraging Azure DevOps, and Azure Pipelines. 

Requirements:

• Advanced degree in data science, computer science, mathematics, engineering, or the natural sciences, or combined 5 years of educational and software engineering experience 
• At least 2 years of hands-on experience with Python and Apache Spark. 
• Expert knowledge of computer science, with strong competencies in data structures, algorithms, software design and distributed computing. 
• Direct experience with at least one of the following: Machine Learning, Natural Language Processing, Deep Learning Models (including Transfer Learning).  
• Strong database experience with at least one RDBMS (SQL Server preferred)  
• Data modeling and design experience in relational and non-relational systems 
• Experience in public cloud platforms (Azure preferred)  
• Strong communication skills with technical and non-technical audiences alike 
• Must be detail-oriented and able to prioritize work and effectively manage multiple tasks 

Experience with any of the following would be a plus: 

• Hands-on experience building and deploying AI/ML models in production settings 
• Storing and managing complex time series and multi-dimensional data and knowledge of associated underlying infrastructure (Microsoft Azure including Storage, secret management, networking) 
• Successful development of machine learning models a distinct plus.