Position Description:
Performs quantitative investment research and data analytics utilizing programming languages such as Python, R, and SQL within the Asset Management division. Conducts sophisticated financial modeling using financial packages and portfolio management tools — FactSet and Bloomberg. Provides research to fund managers through portfolio analyses or empirical studies on new factors and datasets. Builds robust research code and production code in Python, R, and MATLAB. Writes SQL queries to obtain data and create tables, using Snowflake and Oracle database.
Primary Responsibilities:
Leverages financial expertise and research methodologies to support investment needs. Conducts quantitative analyses of information involving investment programs or financial data of public or private institutions, including valuation of businesses. Explores internal data and alternative data to use for quant models or products. Improves the quality and time-to-market of research initiatives using analytical and computational finance development methodologies. Leads the implementation of quantitatively based equity alpha generation, portfolio construction, and risk management analytics. Creates quantitative factors and models to facilitate research and portfolio construction processes. Responds to ad-hoc data analysis requests. Analyzes large data sets in order to develop new processes, perform calculations, identify anomalies, and/or produce new reporting capabilities. Liaises with investment professionals to gather requirements and manage deliverables of technical development teams.Education and Experience:
Master’s degree (or foreign education equivalent) in Financial Engineering, Engineering, Accounting, Economics, Finance, Statistics, Mathematics, or a closely related field and no experience.
Skills and Knowledge:
Candidate must also possess:
Demonstrated Expertise (“DE”) building quantitative models — regression, cross-sectional, and time series in Python and R, using third party vendor data and dynamic parameters — to make recommendations for alpha generation and portfolio construction and to enhance investment decision-making. DE analyzing alternative data to predict company fundamental metrics, develop alpha signals, and build portfolio strategies; and curating large data sets using programming languages and financial software. DE applying multi-factor models for investment research, and implementing predictive analytics and Machine Learning (ML) algorithms (Reinforcement Learning) to develop and backtest trading signals. DE developing written and oral presentations to deliver recommendations during research meetings, communicating complex quantitative findings, and presenting feedback on methodology; and describing and documenting edge cases.#PE1M2
Certifications: