Principal Quantitative Developer

Education and Experience

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
• #PE1M2
• #LI-DNI
• 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.

Benefits that balance life and work

• From our fully paid parent leave to our on-site health and wellness centers, our benefits support the belief that more balance you have, the better you can achieve your goals.

Company overview

Reasonable accommodations

Equal opportunity employer

Applicant screening