Position Description:

Builds robust quantitative tools to aid all aspects of portfolio construction using R, MATLAB, and relational databases. Develops and maintains a set of quantitative tools in R, MATLAB and Python, to help inform asset allocation decisions. Develops and evaluates innovative systematic strategies. Improves frameworks for global tactical asset allocation. Refines tools for outcome-driven portfolio construction. Assists with the design and launch of new products. Meets with clients and consultants to present research. Maintains and improves infrastructure supporting investment process.

Primary Responsibilities:

• Informs investment decisions by analyzing financial information to forecast business, industry, or economic conditions.

• Monitors, measures, and attributes portfolio risks and returns.

• Prepares plans of action for investment, using financial analyses.

• Interprets data on price, yield, stability, future investment-risk trends, economic influences, and other factors affecting investment programs.

• Builds models on the detection and forecast of market environments, providing insights into asset diversification and portfolio construction in distinct regimes.

• Assists with the design and launch of new investment solutions.

• Meets with consultants and clients to present the team’s investment process and quantitative frameworks.

• Collaborates closely with investment and technology professionals within the division.

• Explains complex quantitative concepts to non-technical personnel.

Education and Experience:

Master’s degree (or foreign education equivalent) in Engineering, Accounting, Economics, Finance, Statistics, Mathematics, or a closely related field and no experience.

Skills and Knowledge:

Candidate must also possess:

• Demonstrated Expertise (“DE”) conducting strategic asset allocation research to develop multi-asset solutions for client risk, return, hedging, and longevity needs, including retirement, college savings, and liability driven mandates.

• DE evaluating portfolio construction using mathematical techniques – optimization, linear regression, Monte-Carlo simulations, conditional analysis, and machine learning.

• DE building machine learning models (Hidden Markov Models) in R or Python, for the detection and forecast of market environments, and to provide insights into asset diversification and portfolio construction.

• DE performing financial data analysis and ad-hoc modeling, using Python, MATLAB, or R; performing relative value analysis using financial time series; and performing financial data extraction using SQL queries against relational databases (Oracle).

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