Quantitative Risk Modeler

Quantitative Risk Modeler page is loaded## Quantitative Risk Modeler locations:
Jersey City, NJtime type:
Full time posted on:
Posted 8 Days Agojob requisition :
2120561## ##

Job Description:

** The Role
** Quantitative Research and Investments (QRI) is seeking an experienced quantitative risk professional to contribute to the development and enhancement of Fidelity Asset Management’s risk analytics platform.

The risk analytics platform supports ex-ante risk, VaR, attribution, stress testing and scenario analysis across all asset classes, and is leveraged by investment professionals across Fidelity for risk management, portfolio construction, and alpha research.

The successful candidate will have two separate mandates.  First is to develop the specifications and codebase needed to compute risk analytics not currently supported by the platform, and then collaborate with quantitative developers to deploy these analytics into production. Particular focus will be given to optimizing run-time performance, scalability, and robustness of the core risk calculators on the platform.

The second mandate focuses on research to improve Fidelity’s factor models and specific risk forecasts.  The factor model research will focus on both liquid alternative (e.g. arbitrage, macro, ARP, managed futures) and illiquid alternative (e.g. private credit, infrastructure, real estate, and private equity) asset classes.  A second area of research will focus on collaboration with QRI’s data science researchers to extract insights from alpha research to improve specific risk  addition to focusing on the development of core functionality and methodological improvements, a key part of the role is communicating with portfolio and risk managers across the firm to ensure the platform can be effectively leveraged as part of the investment decision making process.
The successful evolution of the platform will require balancing long term strategic enhancements with tactical enhancements required by the business.

The role sits within the Platform and Analytics Group (PAG) within QRI.  PAG works alongside quantitative researchers in QRI, and across Asset Management, to develop and maintain the infrastructure that enables R&D for alpha generation, risk modeling and portfolio construction. QRI is responsible for the management and development of quantitative investment strategies and solutions while providing high quality quantitative, data-driven support to Fidelity’s fundamental investment professionals, ensuring they have access to the most relevant data and advanced quantitative analysis.
** The Value You Deliver
*** 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
** Education and Experience
*** 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
** The Skills You Bring
*** 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

The…