About The Role

Note: Fidelity will not provide immigration sponsorship for this position.

Position Description

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.

Primary Responsibilities

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.

Education and Experience

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.

Skills and Knowledge

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 RiskMetrics and MSCI Barra; producing Monte Carlo–based risk metrics and stress testing outputs for portfolios and trading strategies, using Python and MSCI RiskMetrics; 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; integrating trading systems, and exchanging and clearing house data and Bloomberg feeds into curated datasets for research and risk reporting, using APIs, SQL, and Python; implementing automated data quality controls including missing data flagging, anomaly detection, and statistical validation, using Python and SQL; maintaining reproducible pipelines and metadata for lineage and auditability, using Snowflake and SQL; and preparing analytic ready datasets for downstream risk modeling, performance reporting, and visualization, using SQL, Snowflake, and Python.
DE designing and implementing automated workflows and batch processing for portfolio risk and performance analytics, derivative product validation, and trading system feature testing, using Python, SQL, Snowflake, Git, and APIs; optimizing code paths and data access using Python and database side SQL; and orchestrating end to end jobs to support quantitative analysis and operational readiness across internal and external systems through APIs and SQL database.

Salary: $155,000.00 to $166,000.00/Year
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Fidelity’s Onsite Working Model

Fidelity is transitioning to a full-time onsite working model through a phased rollout across regions and roles. Currently, some roles and locations require 100% onsite presence, while others require less. Onsite expectations are likely to evolve as the rollout continues. This transition does not apply to fully remote roles.