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Fidelity Bank · 2 months ago

Quantitative Risk Modeler

Jersey City, NJ

Full-time

Hybrid

Mid, Senior Level

$100K/yr - $200K/yr

5+ years exp

Fidelity Bank is seeking an experienced Quantitative Risk Modeler to enhance its risk analytics platform. The role involves developing specifications and code for risk capabilities, collaborating with investment professionals, and conducting research to improve risk forecasts across various asset classes.

BankingBitcoinCustomer ServiceFinancial ServicesInsuranceWealth Management

Responsibilities

Design the specifications and code for core risk capabilities of Fidelity’s risk management platform including decompositions of ex-ante risk, stress testing, attribution, and tail risk

Work with database engineers, software and quantitative developers to deploy new risk analytics into Fidelity’s production environment

Collaborate with investment professionals across Fidelity Asset Management to ensure that risk platform enhancements satisfy end user business requirements

Deliver complex projects with multiple stakeholders

Qualification

Risk managementQuantitative researchStatistical modelingPython programmingSQL data handlingEffective communication

Required

Masters or equivalent experience in Mathematics, Economics, Statistics, Quantitative Finance, or a similarly quantitative field

5+ years of experience within risk management, market risk analytics or quantitative research. A demonstrated ability to partner with both quantitative and fundamental investors and technologists

Prior work experience in financial modeling (e.g., risk models, analytics) or data science and model deployment to production environment is strongly desired

Experience with data handling (ETL, data joining with SQL, cleaning, processing, summarizing, descriptive analysis), and building and back-testing statistical and econometric models

Programming skills in Python and database languages

Experience implementing statistical models that apply cross-sectional and time-series econometrics, dimensionality reduction, and optimization techniques

Demonstrated effective communication with both internal and external stakeholders