Principal Quantitative Developer

Job Description

Fidelity will not provide immigration sponsorship for this position.

Designs and develops investment risk analytics platforms to support quantitative risk analytics and data-driven risk modeling within an investment management context, with a focus on alternative investment products. Develops and maintains linear and non-linear risk analytics to support model calculation, validation, and stress analysis for portfolios and derivative instruments. Develops and enhances risk reporting processes to support derivative exposure measurement, leverage risk monitoring, and Value at Risk (VaR) analysis.

Develops quantitative analytics using Python and SQL to compute portfolio-level risk measures and support ongoing risk monitoring. Supports reporting and visualization solutions using Python-based frameworks to enable effective consumption of portfolio risk analytics. Analyzes, cleanses, and prepares large scale investment and portfolio datasets using statistical and quantitative techniques to support risk analytics and oversight.

• Partners with risk and portfolio managers to deliver quantitative, data-driven investment and portfolio risk solutions across liquid and illiquid alternative investment products.
• Produces quantitative risk reporting and analytics to support monitoring of market, credit, liquidity, and derivatives risks for internal and regulatory purposes.
• Applies quantitative analysis to evaluate portfolio risk characteristics, sensitivities, and profit and loss (PnL) drivers, including those arising from derivative instruments, in support of portfolio construction, hedging, and risk decision-making.
• Develops and maintains models, processes, and workflows used for enterprise risk generation and validation.
• Supports portfolio construction, validation, and reconciliation activities for market-traded and over-the-counter (OTC) instruments.
• Ensures the accuracy, consistency, and reliability of portfolio data used in investment risk analytics and reporting.
• Identifies investment risk management challenges and contributes to data-driven solutions in collaboration with stakeholders.

Bachelor's degree in Quantitative Finance, Finance, Computer Science, Accounting, Management, Financial Mathematics, Actuarial Science, Statistics, or a closely related field (or foreign education equivalent) and five (5) years of experience as a Principal Quantitative Developer (or closely related occupation) performing quantitative and analytical evaluation of portfolio and derivative risk models within an investment management or trading environment to support portfolio construction, and risk management decisions.

Or, alternatively, Master's degree in Quantitative Finance, Finance, Computer Science, Accounting, Management, Financial Mathematics, Actuarial Science, Statistics, or a closely related field (or foreign education equivalent) and three (3) years of experience as a Principal Quantitative Developer (or closely related occupation) performing quantitative and analytical evaluation of portfolio and derivative risk models within an investment management or trading environment to support portfolio construction, and risk management decisions.

Candidate must also possess:
Demonstrated Expertise ("DE") validating and back testing portfolio and derivatives risk models against historical outcomes and benchmarks, using Python, R, and SQL; calibrating and validating model parameters and thresholds for market and derivatives risk measures, including expected shortfall, duration, leverage risk, liquidity risk, derivative exposure, option pricing models, and option sensitivity measures (Greeks), using Python and R; performing factor risk decomposition and non-linear scenario generation, using MSCI Risk Metrics and MSCI Barra;

producing Monte Carlo-based risk metrics and stress testing outputs for portfolios and trading strategies, using Python and MSCI Risk Metrics; and implementing model risk controls and periodic performance reviews through standardized validation scripts and documentation, using Python and R. DE designing standardized and ad hoc risk reporting with risk attribution, performance analysis, and stress testing outputs, using Python and R;

building interactive dashboards and visual analytics for portfolio and derivatives risk, using Python and Power BI; presenting reports to investment teams and senior leadership to communicate exposures, sensitivities, and PnL drivers, using Python and R; translating quantitative results into decision support insights for traders and risk managers, using Python notebooks and presentation templates; and enhancing reporting through automated validations and feedback cycles, using Python, SQL, and APIs.

DE extracting, cleansing, transforming, and validating large scale structured and semi-structured financial data from internal databases and external sources, using SQL, Snowflake, Python, and APIs;…