Model Developer [Multiple Positions Available]

JPMorganChase•Jersey City, WA

4h•Onsite

About The Position

The Model Developer will be responsible for enhancing the analytics framework of the Data Quality Program for market data time series, supporting firmwide Value at Risk models across multiple asset classes. This role involves leading the development and implementation of statistical tools and user applications for end-to-end market data solutions. The developer will also create scalable data storage and monitor pipelines using object-oriented design and distributed computing for risk modeling. Additionally, the role includes analyzing time series construction code, implementing time series construction classes, collaborating with machine learning engineers and Technology teams, responding to technical audit requests, developing secure production code, and mentoring junior developers.

Requirements

Master's degree in Computational Finance, Mathematics, Statistics, or related field of study
Two (2) years of experience in the job offered or as Model Developer, Market Risk Quantitative Researcher, or related occupation.
Two (2) years of experience with developing numerical programs for financial time series analytics using Python and Python libraries including NumPy, Pandas, SciPy, Seaborn, and Matplotlib to process, model, and visualize market data.
Two (2) years of experience building and optimizing SQL queries to extract, transform, and analyze financial time series data from multiple sources.
Two (2) years of experience applying dependency graph programming techniques to manage and process relationships within market data.
Two (2) years of experience designing statistical models to detect data anomalies and ensure integrity in financial datasets, utilizing techniques including correlation analysis, linear regression, and outlier detection algorithms.
Two (2) years of experience performing data engineering and data remediation using quantitative methods including numerical calculus, linear and non-linear interpolation, and proxy filling.
Two (2) years of experience developing scalable data storage and analytical frameworks using object-oriented design and distributed computing to extract, transform, and analyze data used for risk modeling and calculation.
Two (2) years of experience supporting pricing, risk calculations and derived time series construction across Equities, Fixed Income, FX, Commodities, and Structured Products asset classes using financial product knowledge of futures, options, credit default swaps, and securitized products.
Two (2) years of experience estimating financial instrument profit and loss and conducting VaR impact analysis using VaR modeling methods including variance covariance, historical simulation, and Monte Carlo simulation, and sensitivity analysis using delta, gamma, vega, theta, and cross-terms.
Two (2) years of experience enhancing the core calculation framework through code optimization, and performing code review, unit testing, and regression testing while adhering to best coding practices for production deployment.

Responsibilities

Programmatically source and aggregate risk data to assess the materiality and impact of data quality issues for time series.
Develop and maintain advanced models, methodologies and infrastructure to detect anomalies in time series data, such as flats, or spikes, as well as issues related to deficiency in liquidity and data integrity, and implement data remediation techniques.
Enhance the analytics framework of the Data Quality Program for market data time series, supporting firmwide Value at Risk models across multiple asset classes.
Lead the development and implementation of statistical tools and user applications for end-to-end market data solutions.
Develop scalable data storage and monitor pipelines using object-oriented design and distributed computing to extract, transform, and analyze data used for Market Risk and Counterparty Credit Risk modeling and calculation.
Analyze derived time series construction code to establish data lineage and identify issues in the derivation of synthetic time series generated from raw market data.
Implement time series construction class in the core framework of Value at Risk model calculations.
Collaborate with machine learning engineers and Technology teams to deploy data science solutions that enable natural language processing for market data time series.
Respond to technical audit requests and facilitate audit processes to ensure regulatory compliance.
Develop secure, high-quality production code and conduct code reviews for junior developers.
Coach and mentor junior team members and help develop their quantitative and technical skills.