Senior Manager, Quant Data Analytics and Insights

Designs and maintains Python-based data abstraction and analytical tools to streamline and standardize quantitative processes. Responds to ad-hoc data analysis requests in support of projects performed by quant analysts. Contributes to the continued development and incorporation of non-traditional and unstructured data, as well as data science applications, to enhance the research and investment process. Programs in Python or R with the ability to develop customized quantitative tools. Works with FactSet and Bloomberg. Back-tests Quant Platform capabilities. Runs Cloud technologies. Supports production data management and model integrity, and provides end-to-end technical support for portfolio management, risk models, and quant research platforms. Develops quantitative techniques to inform securities investing, equities investing, pricing, or valuation of financial instruments. Develops mathematical or statistical models for risk management, asset optimization, pricing, or relative value analysis.

Responsibilities

Conducts investment risk forecasting and portfolio construction, including model construction, factor definitions, factor calculations, and translates output statistics into meaningful information; Develops and operates data platforms to deliver production-grade data curation to be utilized in quantitative models, portfolio construction, and trading; Supports production reporting cycles across analytic environments; Ensures input data integrity, availability, and completeness; verifies successful process execution, accurate statistical output, and proper report generation; Produces new reporting capabilities; Works with quantitative analytics (fund and index performance measurement) and Security Reference data across asset classes and derivative instruments; Computes core statistical measures; Provides analytic solutions and reporting frameworks to business partners and senior leadership; Expands and improves technical and data infrastructure, research platforms, diagnostic tools, and reporting for multi-factor model risk forecasting, quantitative alpha models, big data analytics, and performance attribution; Designs and maintains Python-based data abstraction and analytical tools; Responds to ad-hoc data analysis requests in support of quant analysts; Back-tests Quant Platform capabilities; Runs Cloud technologies; Supports production data management and model integrity, and provides end-to-end technical support for portfolio management, risk models, and quant research platforms

Requirements

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 Senior Manager, Quant Data Analytics and Insights (or closely related occupation) performing analysis to evaluate portfolio investments against client guidelines and firm compliance controls, using Python, SQL, and R; OR Master's degree in Computer Science, Engineering, Information Technology, Information Systems, or a closely related field (or foreign education equivalent) and three (3) years of experience as a Senior Manager, Quant Data Analytics and Insights (or closely related occupation) performing analysis to evaluate portfolio investments against client guidelines and firm compliance controls, using Python, SQL, and R; Demonstrated expertise supporting large quantitative platforms as a product owner using Stash; facilitating stand ups, sprint planning, retrospectives, and backlog grooming, using Jira; writing risk model methodologies and specification documents, interpreting business requirements, and creating analytical visualization solutions, using Confluence and Visio; Demonstrated expertise analyzing security analytics across asset classes (equities, options, swaps, commodities, fixed income) using BarraOne or FactSet; analyzing Barra Risk Factor Model construction, factor definitions and calculations, using BarraOne UI; Demonstrated expertise supporting portfolio managers and quantitative analysts by performing portfolio performance and risk reporting, using Oracle SQL, Tableau, and Excel; calculating parametric VaR, duration, Monte Carlo simulations, and stress testing, using Oracle SQL, Python, and BarraOne; Demonstrated expertise spearheading design, implementation, or ongoing operations of data quality frameworks for financial data systems on multi-terabyte data sets, using SQL, R, and Python; establishing KPIs for internal and external vendor teams, using Oracle SQL and Excel; implementing automated checks and monitoring systems, using Autosys, SQL, and Python