Job Description

Position Description:

Oversees the development of high quality, robust, and efficient analytical solutions to define risk for alternative investment portfolios. Drives the implementation of quantitative research projects throughout the Software Development Lifecycle (SDLC) according to a full-stack implementation approach, using R, Python, PL/SQL databases, and Artificial intelligence (AI) / Machine Learning (ML) toolkits. Builds high quality, robust, and efficient analytical and software solutions to improve investment processes. Analyzes information to determine, recommend, and plan installation of new systems or modification of existing systems. Directs the teams on projects, including portfolio construction, risk management, alpha research, and new quantitative product software development. Generates new insights to support research teams in developing new models and products that will provide an advantage to the organization in the marketplace, using R, Python, SQL, and Linux. Oversees initiatives involving portfolio risk management software and collaborations with cross-functional teams.

Primary Responsibilities:

Identifies business opportunities and evaluates best approaches for predictive or prescriptive analytics.
Gathers internal and external data (structured or unstructured) from multiple sources using programming languages.
Implements best practices for model development, iteration, and code management.
Conducts code reviews.
Draws key business insights from advanced quantitative analyses and presents findings to broader audience and senior management.
Supports junior team members in learning and developing analytical solutions.
Develops and implements a set of techniques or analytics applications to transform raw data into meaningful information, using data-oriented programming languages and visualization software.
Applies data mining, data modeling, natural language processing, and machine learning for extracting and analyzing information from large structured and unstructured datasets.
Visualizes, interprets and reports data findings.
Develops analytical models using data analytics and software development methodologies.
Applies analytics and quantitative concepts to support investment needs and develop new solutions.
Identifies and acquires strategic talent needs, resource planning, and talent allocation across the portfolio.
Manages multiple projects and programs.
Mentors and coaches junior Quant Developers.

Education and Experience:

Bachelor’s degree in Computer Science, Engineering, Information Technology, Information Systems, or a closely related field (or foreign education equivalent) and five (5) years of experience as a Director, Quant Developer (or closely related occupation) developing software solutions (risk management, portfolio construction, performance analysis, or alpha research) in a financial services environment, using Python and Python data libraries to reorder concepts.

Or, alternatively, Master’s degree in Computer Science, Engineering, Information Technology, Information Systems, or a closely related field (or foreign education equivalent) and four (4) years of experience as a Director, Quant Developer (or closely related occupation) developing software solutions (risk management, portfolio construction, performance analysis, or alpha research) in a financial services environment, using Python and Python data libraries to reorder concepts.

Skills and Knowledge:

Candidate must also possess:

Demonstrated Expertise (“DE”) performing full scale development of portfolio risk management software (using Monte-Carlo simulations, Python, Pandas, Git, and SQL) and Cloud deployment supporting portfolio managers, trading desks, performance, and risk analysts.
DE performing financial calculations, data validation, and data modelling using data from financial data vendors (Bloomberg, Axioma, FactSet, and Barra); and accessing database tables using SQL queries to outright errors to inconsistencies that are visible only through active modelling.
DE applying financial concepts (risk attribution, risk decomposition, or risk aggregation) to support and enhance portfolio risk analytics when developing applications, subsystems, services, or scripts, using Python or R tools.
DE developing and supporting quantitative software using quantitative concepts applicable to distinct financial instruments (bonds, stocks, swaps, or options), and Python or R tools.

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