Model Developer [Multiple Positions Available]

DESCRIPTION:

Duties: 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.

QUALIFICATIONS:

Minimum education and experience required: Master's degree in Computational Finance, Mathematics, Statistics, or related field of study plus two (2) years of experience in the job offered or as Model Developer, Market Risk Quantitative Researcher, or related occupation.

Skills Required: This position requires two (2) years of experience with the following: 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; Sup...