Model Developer [multiple Positions Available]

What you'd actually do

1. Programmatically source and aggregate risk data to assess the materiality and impact of data quality issues for time series.
2. 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.
3. Enhance the analytics framework of the Data Quality Program for market data time series, supporting firmwide Value at Risk models across multiple asset classes.
4. Lead the development and implementation of statistical tools and user applications for end-to-end market data solutions.
5. 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.

Skills

What the JD emphasized

• Master's degree in Computational Finance, Mathematics, Statistics, or related field of study plus two (2) years of experience
• 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.

Other signals

• Develop and maintain advanced models, methodologies and infrastructure to detect anomalies in time series data
• Enhance the analytics framework of the Data Quality Program for market data time series
• Collaborate with machine learning engineers and Technology teams to deploy data science solutions that enable natural language processing for market data time series