Job Description:Work closely with discretionary investors to leverage scientific insights that enhance investment goals and outcomes. Work with other scientific investors and resources within Investment Science Department (iSci) and across the firm. Leverage resources within iSci, comprised of risk managers, data scientists, quantitative investors, microstructure, and derivatives experts, as well as their own expertise to bring a spectrum of scientific insights to investment processes and philosophies. Specifically, perform analysis to provide critical scientific insights and ideas in execution of investment strategies across a diverse set of discretionary strategies. Develop new models, custom analytics, and technologies that enhance investment process and investment team's productivity. Work with Portfolio Managers to improve capital allocation and other portfolio construction decisions using risk models and analytical tools. Analyze and propose optimal hedging strategies where appropriate. Work with broader Investment Science Department to design and test new tools and workflow. Analyze Portfolio Management behavior, liquidity, and trading costs to improve efficiency of trading and after-cost results utilizing tools, such as time-series and cross-sectional statistics, econometrics, optimization techniques, and stochastic simulation. Position is fixed location based in the New York office; however, telecommuting from a home office may also be allowed. 25% domestic travel to visit other Wellington offices.Job Requirements:Requires a Master's degree (or foreign equivalent) in Computer Science, Financial Mathematics, Financial Economics, Financial Engineering or a directly related field plus four (4) years of experience with multiple asset classes, equity, derivatives, and ETFs. Experience must include:Four (4) years of experience in each of the following (experience may be gained concurrently): - Modeling of financial markets utilizing simulation and optimization, including Monte Carlo methods- Utilizing ML techniques, including penalized regression, clustering techniques, distance models, trees, or neural nets- Probability, statistics, time-series analysis, and cross-sectional analysis, including very large data sets and SQL- Modeling of financial markets utilizing backtests and simulations- Experience with Python and SQL coding, queries, data running, and databases/servers- Extensive use of data aggregators, including Bloomberg, FactSet, and Barra, through user interfaces and APIsTwo (2) years of experience in each of the following (experience may be gained concurrently): - Experience with alternative data sets for backtesting and individual stock analyses.