What are the responsibilities and job description for the Quantitative Analyst position at Loomis Sayles?
About the Role
Support Portfolio Construction, Trade Execution, Trading Cost Analysis, Model Delivery, and ad-hoc statistical analysis. Leverage PostgreSQL and SQL Server to develop database architectures to store and manipulate large datasets. Leverage C# and other programing user interfaces to automate complex processes, develop and back-test portfolio construction and execution strategies, and display complex data to non-technical stakeholders.
Job Responsibilities
Collaborate on the architecture and development of a front-office investment platform with an emphasis on systematic solutions to support scalability and customization across a broad product suite.
Enhance operational efficiency by applying software engineering methodologies to streamline existing complex processes related to order creation and trade execution.
Apply mathematical and statistical techniques in developing analytical tools to display accurate performance, attribution, profit and loss measurement, and pricing of complex portfolio structures in a real-time pricing environment.
Provide in-person application and analytical support, while applying proper source code management to implement enhancements in a production environment.
Education and Experience
Master of Science degree (or foreign education equivalent) in Engineering, Computer Science, Information Systems, Mathematics, Physics, or a closely related field and two (2) years of experience as a Quantitative Analyst (or closely related occupation) building software applications and performing data analytics in a financial services environment.
Skills and Knowledge
Candidate must also possess:
Demonstrated Expertise (“DE”) in leveraging data-driven approaches to analyze and evaluate investment and trading strategies across various financial instruments, including fixed income, equity and derivatives while utilizing advanced data analytics techniques and risk methodologies such as stress testing and scenario analysis.
DE integrating large datasets of financial or non-financial information into existing data framework, and presenting summarized or detailed findings to senior stakeholders to support and improve investment decisions; and using Python or API development and BI tools for advanced data visualization.
DE developing and conducting quantitative data and risk analyses – statistical regression modeling, portfolio optimization, Monte Carlo simulation, Value-at-Risk (VaR), and liquidity risk, using Python, R, Bloomberg, and Excel.
DE interfacing directly with traders, portfolio managers or other end users, and independently developing robust data warehouse utilizing Python, C#, and SQL to architect solutions to non-technical stakeholders.
