Job Description:

Position Description:

Builds quantitative models and tools to support and strengthen the investment process used to manage client accounts. Develops and enhances statistical models using Python. Performs advanced data analysis using SQL, Bloomberg terminal, and Bloomberg API. Maintains existing methodologies and develops new methodologies for portfolio construction using optimization theory and frameworks (SciPy and CVXPY). Advises portfolio managers on suitable investment options by conducting risk-adjusted return analyses at security and sector levels. Assists portfolio managers in managing risk by building tools to measure and hedge risk exposures. Builds tools to measure and attribute performance of portfolios relative to their benchmarks. Provides research and analysis to portfolio managers and other investment professionals in support of a broad range of investment solutions.

Primary Responsibilities:

• Maintains and enhances analytics infrastructure related to Agency and Non-agency Mortgage-backed and Asset-backed securities.
• Maintains and enhances statistical models used in valuation of Securitized products (mortgage rate model, prepayment model, default model).
• Monitors updates in data disclosures, academic and sell-side research related to securitized products modeling.
• Monitors the quality of analytics generated by models.
• Assists in understanding and debugging anomalies.
• Builds robust quantitative tools to aid all aspects of portfolio analysis and construction.
• Collaborates closely with other investment and technology professionals within the division.
• Explains complex quantitative concepts to non-technical audiences.
• Monitors, measures, and attributes portfolio risks and returns.
• Responds to ad-hoc data analysis requests from Portfolio Managers.
• Publishes thought leadership and whitepapers.

Education and Experience:

Bachelor's degree in Computer Science, Engineering, Information Technology, Information Systems, Computational Finance, Economics, or a closely related field (or foreign education equivalent) and five (5) years of experience as an AM Quantitative Analyst II (or closely related occupation) performing quantitative analysis in support of portfolio management within an asset management and investment products environment.

Or, alternatively, Master’s degree in Computer Science, Engineering, Information Technology, Information

Systems, Computational Finance, Economics, or a closely related field (or foreign education equivalent) and three (3) years of experience as an AM Quantitative Analyst II (or closely related occupation) performing quantitative analysis in support of portfolio management within an asset management and investment products environment.

Skills and Knowledge:

Candidate must also possess:

• Demonstrated Expertise (“DE”) conducting end-to-end research and analysis in factor-based investing, using Python and R; extracting data using Bloomberg and SQL; creating cross sectional and time series signals for multi-asset portfolios, using Tidyverse and Pandas; executing portfolio sizing using optimization routines -- nloptr, Scipy, and CVXPY; back-testing strategies and innovating metrics to assess strategy performance; communicating complex research findings to portfolio managers; and maintaining codebase according to best practices using Git.
• DE constructing multi-asset portfolios and benchmarks for a variety of investment objectives and risk profiles, using Black-Litterman and Mean-Variance optimization techniques in R; designing multi-asset inflation hedged portfolios with MinMax optimization, to maximize expected returns while minimizing downside risk during inflationary stress periods, using CVXPY; and backfilling asset returns time series data using linear regression, Hidden Markov Models (HMM), and Conditional Monte Carlo (CMC) simulations to back-test the resulting portfolios using Python.
• DE fitting models to assess the impact of a loan servicer or originator on the prepayment speed of a mortgage (via the multiplier approach), using MATLAB; aggregating loan level data to bond level for millions of bonds (Mortgage Backed Securities (MBS) and Collateralized Mortgage Obligations (CMO)), using Snowflake and SQL; estimating risk betas for MBS pools and CMOs, using empirical and analytical methods; and collaborating with traders to enhance tools and analytics to compare bonds in the cross section, using VBA and Excel.
• DE modeling non-agency fixed income securitized products; inferring implied volatility surfaces using Python, root finding techniques, and Black-Scholes formula; calibrating stochastic models using market data; and developing pricing models for non-agency instruments (Commercial Mortgage-Backed Indices (CMBX), Commercial Mortgage-Backed Securities (CMBS), and Asset-Backed Securities (ABS)), using Intex, Monte Carlo simulations, and variance reduction techniques.

[Experience and/or expertise may be gained during graduate degree program]

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Certifications:

Category:

Investment Professionals

Most roles at Fidelity are Hybrid, requiring associates to work onsite every other week (all business days, M-F) in a Fidelity office. This does not apply to Remote or fully Onsite roles. Some roles may have unique onsite requirements. Please consult with your recruiter for the specific expectations for this position.

Please be advised that 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.

Benefits

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