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About the Role
Designs and maintains Python-based data abstraction and analytical tools to streamline quantitative processes. Responds to ad-hoc data analysis requests and contributes to the development of non-traditional data and data science applications to enhance the research and investment process. Supports production data management, model integrity, and provides end-to-end technical support for portfolio management, risk models, and quant research platforms. Develops quantitative techniques and mathematical/statistical models for securities investing, pricing, risk management, and asset optimization.
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 for 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.
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.
- 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; 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 asset classes, including equities, options, swaps, commodities, and fixed income (including swaps and security types in fixed income including treasuries, corporates, Municipal bonds, and securitized products) using BarraOne or FactSet; covering funds across active, systematic, open and closed architecture asset allocation, and risk modelling, using Bloomberg PORT, MSCI Barra’s equity models, and Barra Multi Asset Class model; and analyzing the Barra Risk Factor Model construction, factor definitions and calculations, and translating output statistics into portfolio management insights, using BarraOne UI.
- Demonstrated Expertise supporting portfolio managers and quantitative analysts by performing portfolio performance and risk reporting, using Oracle SQL, Tableau, and Excel; building and enhancing reports, using Python, R, and Excel VBA; and calculating parametric VaR, duration, Monte Carlo simulations, and stress testing for regulatory purposes, using Oracle SQL, Python, and BarraOne.
- Demonstrated Expertise spearheading design, implementation, or ongoing operations of robust data quality frameworks for financial data systems on multi-terabyte data sets, using SQL, R, and Python; establishing Key Performance Indicators (KPIs) for internal and external vendor teams and driving improvement, using Oracle SQL and Excel; and collaborating cross functionally with development teams to ensure data integrity, enforce data quality checks, and procedures to maintain accuracy and consistency, and implementing automated checks and systems for monitoring, using Autosys, SQL, and Python.
Skills
- Python
- R
- FactSet
- Bloomberg
- Cloud technologies
- Stash
- Jira
- Confluence
- Visio
- BarraOne
- Bloomberg PORT
- MSCI Barra’s equity models
- Barra Multi Asset Class model
- Oracle SQL
- Tableau
- Excel
- Excel VBA
- Autosys
Location
- Onsite
Work Type
- Full-time
- Onsite
Experience Level
- 5 years
- 3 years
Education Level
- Bachelor’s degree in Computer Science, Engineering, Information Technology, Information Systems, or a closely related field
- Master’s degree in Computer Science, Engineering, Information Technology, Information Systems, or a closely related field
Salary/Compensations
- $145,800.00 to 181,000.00 /year
About the Company
- Fidelity’s business is governed by the provisions of the Securities Exchange Act of 1934, the Investment Advisers Act of 1940, the Investment Company Act of 1940, ERISA, numerous state laws governing securities, investment and retirement-related financial activities and the rules and regulations of numerous self-regulatory organizations, including FINRA, among others.
- Those laws and regulations may restrict Fidelity from hiring and/or associating with individuals with certain Criminal Histories.