AM Quantitative Analyst I

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: Master’s degree (or foreign education equivalent) in Accounting, Computational Finance, Economics, Finance, Mathematics, Mathematical Finance, Statistics, and no experience. Skills and Knowledge: Candidate must also possess: Demonstrated Expertise (“DE”) conducting factor investment research and end-to-end analysis 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 for implementation; and maintaining codebase according to best practices using Git. DE gathering and analyzing data to extract market signals or insights: web-scraping non-structured textual data (for example 10Qs and 10Ks from the SEC EDGAR website and social media) using Beautiful Soup, APIs, and public libraries in Python; pulling structured market index data from the CRSP (Center for Research in Security Prices) database and Bloomberg; preprocessing data using Pandas; and training supervised and unsupervised models using Natural Language Processing (NLP) models to quantify market sentiment using customized dictionaries and pre-trained neural networks. DE forecasting returns using time series and Autoregressive Integrated Moving Average (ARIMA) models; training neural networks using Scikit-Learn and Tensorflow; extracting market regimes for trading purposes using Hidden Markov Models (HMM) and Gaussian Mixture Models (GMM); implementing portfolio risk modeling using Python; computing risk metrics -- Value-At-Risk (VaR), Counterparty Valuation Adjustments (CVA) -- and automating Fixed Income Profit and Loss (PnL) calculations. DE inferring implied volatility surfaces using Python, root finding techniques, and Black-Scholes formula; calibrating stochastic models using market data; and pricing exotic derivative securities using Monte Carlo simulations using variance reduction techniques. [Expertise may be gained during graduate degree program]. #PE1M2 Certifications: Category: Investment Professionals Fidelity’s hybrid working model blends the best of both onsite and offsite work experiences. Working onsite is important for our business strategy and our culture. We also value the benefits that working offsite offers associates. Most hybrid roles require associates to work onsite every other week (all business days, M-F) in a Fidelity office. At Fidelity, we are passionate about making our financial expertise broadly accessible and effective in helping people live the lives they want! We are a privately held company that places a high degree of value in creating and nurturing a work environment that attracts the best talent and reflects our commitment to our associates. We are proud of our diverse and inclusive workplace where we respect and value our associates for their unique perspectives and experiences. For information about working at Fidelity, visit FidelityCareers.com. Fidelity Investments is an equal opportunity employer. Fidelity will reasonably accommodate applicants with disabilities who need adjustments to participate in the application or interview process. 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