Director, Portfolio Analytics
$165,000–$194,000 year
On-siteBoston, Massachusetts, United States
Job Summary
Oversee production data management, model integrity, and end-to-end technical support for portfolio management, risk models, and quantitative research platforms. Build quantitative research platforms and tools, develop new processes, and produce reporting capabilities by analyzing large data sets. Evaluate new content through proofs-of-concept, extend data platforms, and translate analytics into model construction and factor definitions. Perform validations and testing of models, present mathematical modeling results to management, and advise senior management on technical strategy. Set vision and direction for the team, lead organization-wide initiatives, and provide technical supervision to multiple teams on complex projects. This role requires a Director-level background in multi-asset class analytics within a financial services environment. Fidelity will not provide immigration sponsorship for this position.
Required Qualifications
- Bachelor's degree in Computer Science, Engineering, Information Technology, Information Systems, or a closely related field (or foreign education equivalent)
- Six (6) years of experience as a Director, Portfolio Analytics (or closely related occupation) performing multi-asset class analytics in a financial services environment
- Master's degree in Computer Science, Engineering, Information Technology, Information Systems, or a closely related field (or foreign education equivalent)
- Four (4) years of experience as a Director, Portfolio Analytics (or closely related occupation) performing multi-asset class analytics in a financial services environment
- Demonstrated Expertise performing product management and impact analysis on trading and risk analytics in a complex multi-asset class environment (open and closed architecture fund-of-funds structures)
- Debugging differences between economic and accounting views of holdings and assessing their influence on portfolio construction
- Monitoring and measuring risk for mutual funds
- Writing specification documents, interpreting business requirements, and creating analytical visualization solutions
- Using Microsoft Office suite, Visual Basic (VBA), Tableau and Oracle SQL
- Defining and maintaining operational workflows
- Creating data quality assurance tools for validating holdings from various vendors (Morningstar, Bloomberg, Index providers and accounting views) and instrument data across multi-asset classes (Equity, Corporate Bonds, Municipal Bonds, Commodities, Derivatives, and Alternative strategies)
- Using Oracle SQL, Snowflake, Python, Microsoft Excel, and Visual Basic (VBA)
- Cleaning, validating, cross-checking, tracking, and documenting changes, anomalies, and inconsistencies in raw data for investment risk analysis
- Analyzing BarraOne Multi-Asset Class (MAC) risk factor model construction, factor definitions, and calculations
- Translating equity, fixed income, alternatives risk and stress analytics into meaningful insights used for portfolio maintenance
- Integrating BarraOne risk models into existing infrastructure, and expanding and enhancing analytic platforms and tools
- Using Microsoft Excel, Snowflake, Tableau, Oracle SQL, Python, and R
- Performing investment risk analysis (portfolio performance and risk reporting)
- Using BarraOne Multi-Factor Model (MAC) risk and performance attribution
- Measuring incremental effects of portfolio construction methodologies
- Performing fund holdings and returns analysis
- Using statistical methods and analytical tools – Analysis of Variance (ANOVA) and Analysis of Covariance (ANCOVA)
- Writing risk model methodologies and specification documents, interpreting business requirements, and creating model performance monitoring reports in Confluence and Microsoft Office Suite
- Integrating portfolio management workflows into vendor tools (Factset and MSCI BarraOne)
- Using Microsoft Office Suite, Tableau, Oracle SQL, Snowflake, Python, and R
- Researching and testing methods to improve existing models
- Using regression techniques, time series analysis, and interest rate models and Monte Carlo simulations
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