Model Developer [Multiple Positions Available]

JPMorgan Chase & Co.•Jersey City, NJ

2h•$160,000 - $238,000•Onsite

About The Position

The Model Developer will be responsible for programmatically sourcing and aggregating risk data to assess the materiality and impact of data quality issues for time series. This role involves developing and maintaining 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. The developer will also implement data remediation techniques and enhance the analytics framework of the Data Quality Program for market data time series, supporting firmwide Value at Risk models across multiple asset classes. Key responsibilities include leading the development and implementation of statistical tools and user applications for end-to-end market data solutions, developing scalable data storage and monitoring pipelines using object-oriented design and distributed computing, and analyzing derived time series construction code to establish data lineage and identify issues. The role also involves implementing time series construction classes in the core framework of Value at Risk model calculations, collaborating with machine learning engineers and Technology teams to deploy data science solutions for natural language processing, responding to technical audit requests, and developing secure, high-quality production code. Additionally, the Model Developer will conduct code reviews for junior developers and mentor them to develop their quantitative and technical skills.

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.
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
Building and optimizing SQL queries to extract, transform, and analyze financial time series data from multiple sources
Applying dependency graph programming techniques to manage and process relationships within market data
Designing statistical models to detect data anomalies and ensure integrity in financial datasets, utilizing techniques including correlation analysis, linear regression, and outlier detection algorithms
Performing data engineering and data remediation using quantitative methods including numerical calculus, linear and non-linear interpolation, and proxy filling
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
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
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
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.