Position Description:

Provides analytics solutions and data management services related to fixed income, equities, asset allocation, and broad economic conditions to quantitative researchers and portfolio managers. Develops and implements proprietary quantitative investment and risk analytics tools using SQL, Python, VBA, and R. Develops deep understanding and practical skills across all aspects of quantitative investment and portfolio risk including model construction, factor and covariance definitions, factor calculations, and translates output statistics into meaningful information used within the portfolio management function. Participates in development efforts to expand and enhance the technical infrastructure and reports capabilities, multi-factor model risk forecasting, quantitative alpha research, big data analytics, and performance attribution. Serves as integral part of project teams to deliver innovative data visualization and analytic tools capable of illustrating current and time series investment themes, portfolio exposures, and factors driving fund performance using Python, Power BI, and Tableau. Leverages Quant Platform capabilities including back-testing and computing core statistical measures and leveraging Cloud technologies to run these capabilities.

Primary Responsibilities:

• Performs database management and coordinates production reporting cycles across the analytic environments.

• Performs analysis, design, and quality assurance functions across efforts to acquire new datasets, advance analytic capabilities, and produce informative content used across quantitative research.

• Leverages SQL to transverse a wide array of database schemas.

• Develops excel VBA with embedded SQL queries to automate analytics and reporting functions for quantitative research.

• Maintains and enhances the portfolio construction process in Python, incorporating new features such as risk exposures, market share analysis, and corporate actions monitoring into the existing infrastructure.

• Develops and enhances python tools and library for customized aggregated ETF holdings and exposure score back-testing; conducts annual review, quarterly mock rebalance, and annual review in Python, and publishes analytical statistics to Power BI dashboards widely utilized across quantitative research.

• Monitors and optimizes systematic equity portfolio construction process for SMA/ETFs.

• Develops and runs data quality monitoring algorithms to ensure the accuracy of data delivery to portfolio managers.

• Contributes to the continued development and incorporation of non-traditional and/or unstructured data as well as data science applications to enhance the research and investment process.

• Implements and enhances quantitative investment and risk analytic models utilizing statistical modeling techniques and evaluates model performance using financial risk matrix.

• Delivers innovative data visualization and analytic tools capable of illustrating investment themes, portfolio exposures, and factors driving fund performance.

• Develops model validation process and data quality checks in Python and sets up jobs in Autosys.

• Responds to ad-hoc data analysis, data visualization, and back-testing requests in support of projects performed by QRI.

Education and Experience:

Bachelor’s degree (or foreign education equivalent) in Mathematics, Physics, Financial Economics, Economics, Financial Engineering, Statistics, or a closely related field and five (5) years of experience as a Senior Quantitative Specialist (or closely related occupation) performing quantitative research analytics and financial modeling related to equity, fixed income, and asset allocation using Python and R.

Or, alternatively, Master’s degree (or foreign education equivalent) in Mathematics, Physics, Financial Economics, Economics, Financial Engineering, Statistics, or a closely related field and three (3) years of experience as a Senior Quantitative Specialist (or closely related occupation) performing quantitative research analytics and financial modeling related to equity, fixed income, and asset allocation using Python and R.

Skills and Knowledge:

Candidate must also possess:

• Demonstrated Expertise (“DE”) developing and maintaining data solutions, platforms, and workflows using Bloomberg, FactSet, SQL, Python and VBA to support quantitative research on portfolios and indices and portfolio operations; and performing integration, quality checks, and analytics on data used in model development and production leveraging VBA, SQL, R, and Python.

• DE performing statistical modeling to develop and enhance quantitative models including -- linear and logistic regression, time series, multi-factor risk, and ARCH /GARCH models of financial and fundamental data -- utilizing Stata and R; conducting model performance analytics using mathematical and financial matrix -- R-squared and Sharpe ratio -- to validate the statistical robustness of financial models and evaluate investment strategy performance; and performing portfolio sensitivity analysis including Monte-Carlo simulation to predict future portfolio risk/returns under hypothetical scenarios.

• DE performing portfolio and risk analytics, including portfolio return and turnover analysis, and risk analytic projections that calculate portfolio standard deviation, tracking error, equity beta, duration risk, sector risk, interest rate risk, systematic and firm specific risk, and factor risk exposures across equity, fixed income, and multi assets using Bloomberg, VBA, Stata, and R.

• DE designing and developing data visualization tools, interactive web interfaces and applications for investment and risk analytics, management reporting, and portfolio analysis to support portfolio construction process and active asset allocation strategies using SQL, SSRS, Python, and R.

#PE1M2

#LI-DNI