Senior Manager, Quant Data Analytics and Insights

Fidelity•San Francisco, CA

About The Position

Designs and maintains Python-based data abstraction and analytical tools that streamline and standardize quantitative processes, and responds to ad-hoc data analysis requests supporting quantitative analysts. Contributes to incorporating non-traditional and unstructured data and data science applications into research and investment processes. Programs in Python or R to develop customized quantitative tools, works with FactSet and Bloomberg, back-tests Quant Platform capabilities, and runs cloud technologies. Provides production data management, model integrity, and end-to-end technical support for portfolio management, risk models, and quantitative research platforms. Develops quantitative techniques and mathematical or statistical models for securities and equities investing, pricing, valuation, risk management, asset optimization, and relative value analysis. The role conducts investment risk forecasting and portfolio construction, including model construction, factor definitions and calculations, and interpretation of output statistics. It develops and operates data platforms for production-grade data curation used in quantitative models, portfolio construction, and trading. It supports production reporting cycles, ensures input data integrity and process execution, verifies statistical outputs and report generation, and produces new reporting capabilities. The position also works with fund and index performance measurement and security reference data across asset classes and derivatives, computes core statistical measures, and delivers analytical solutions and reporting frameworks to business partners and senior leadership. The role 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. Fidelity states that it will not provide immigration sponsorship for this position. The company is transitioning toward a full-time onsite working model through a phased rollout; onsite expectations vary by role and location and may evolve.

Requirements

Bachelor’s degree in Computer Science, Engineering, Information Technology, Information Systems, or a closely related field (or foreign education equivalent), plus five years of experience as a Senior Manager, Quant Data Analytics and Insights or in a closely related occupation performing analysis to evaluate portfolio investments against client guidelines and firm compliance controls using Python, SQL, and R; alternatively, a master’s degree in one of those fields (or foreign education equivalent) plus three years of such experience.
Demonstrated expertise supporting large quantitative platforms as a product owner using Stash; facilitating stand-ups, sprint planning, retrospectives, and backlog grooming using Jira; and 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 equities, options, swaps, commodities, and fixed income—including treasuries, corporates, municipal bonds, and securitized products—using BarraOne or FactSet.
Demonstrated expertise covering funds across active and systematic approaches, open and closed architecture asset allocation, and risk modeling using Bloomberg PORT, MSCI Barra equity models, and the Barra Multi Asset Class model.
Demonstrated expertise analyzing Barra Risk Factor Model construction, factor definitions and calculations, and translating output statistics into portfolio management insights using the BarraOne UI.
Demonstrated expertise performing portfolio performance and risk reporting for portfolio managers and quantitative analysts using Oracle SQL, Tableau, and Excel.
Demonstrated expertise building and enhancing reports using Python, R, and Excel VBA.
Demonstrated expertise calculating parametric VaR, duration, Monte Carlo simulations, and stress testing for regulatory purposes using Oracle SQL, Python, and BarraOne.
Demonstrated expertise designing, implementing, or operating robust data quality frameworks for financial data systems on multi-terabyte datasets using SQL, R, and Python.
Demonstrated expertise establishing KPIs for internal and external vendor teams and driving improvement using Oracle SQL and Excel.
Demonstrated expertise collaborating cross-functionally with development teams to ensure data integrity, enforce data quality checks and procedures, and maintain accuracy and consistency; implementing automated checks and monitoring systems using Autosys, SQL, and Python.

Responsibilities

Design and maintain Python-based data abstraction and analytical tools to streamline and standardize quantitative processes.
Respond to ad-hoc data analysis requests in support of quantitative analysts.
Contribute to the incorporation of non-traditional and unstructured data and data science applications into research and investment processes.
Program in Python or R and develop customized quantitative tools.
Work with FactSet and Bloomberg, back-test Quant Platform capabilities, and run cloud technologies.
Support production data management, model integrity, and end-to-end technical support for portfolio management, risk models, and quantitative research platforms.
Develop quantitative techniques for investing, pricing, and valuation, and mathematical or statistical models for risk management, asset optimization, pricing, or relative value analysis.
Conduct investment risk forecasting and portfolio construction, including model construction, factor definitions and calculations, and translating output statistics into meaningful information.
Develop and operate data platforms that deliver production-grade data curation for quantitative models, portfolio construction, and trading.
Support production reporting cycles across analytic environments.
Ensure input data integrity, availability, and completeness; verify successful process execution, accurate statistical output, and proper report generation.
Produce new reporting capabilities.
Work with quantitative analytics for fund and index performance measurement and with security reference data across asset classes and derivative instruments.
Compute core statistical measures and provide analytical solutions and reporting frameworks to business partners and senior leadership.
Expand and improve 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.
Support large quantitative platforms as a product owner, facilitating agile ceremonies and managing platform work and documentation.
Analyze security analytics and risk models across asset classes and support portfolio managers and quantitative analysts with portfolio performance and risk reporting.
Design, implement, or operate data quality frameworks for financial data systems and collaborate with development teams to maintain data integrity and accuracy.