Model Developer [Multiple Positions Available]

DESCRIPTION:

Duties: Oversee the daily calculation of Average Daily Trading Volume and address analytical issues to ensure the timely delivery of high-quality data essential for setting Counterparty Credit Risk limits.

Lead implementation projects by overseeing analytical work and reviewing code produced by junior developers.

Coach and mentor junior team members and help develop their quantitative and technical skills.

Develop and maintain advanced models, methodologies and infrastructure to detect anomalies in time series data, such as flats, spikes, as well as issues related to deficiency in liquidity and data integrity and implement data remediation techniques.

Analyze and improve the performance of outlier detection and missing data imputation tools.

Enhance the analytics framework of the Data Quality Program for market data time series, supporting firmwide Value at Risk models across multiple asset classes.

Develop, maintain and enhance APIs and visualization tools used for time series data management and analysis.

Design and develop a scalable framework that can easily onboard new data source while adapting to evolving analytics needs.

Create data quality metrics and KPIs to assess data quality, identify trends and areas for improvement, and communicate findings to senior management and internal control functions.

Respond to audit requests from external and internal audits, regulatory exams, and risk control managers.

Understand methodologies and debug implementation code to establish data lineage and identify issues in the derivation of synthetic time series generated from raw time series data.

QUALIFICATIONS:

Minimum education and experience required: Master's degree in Computational Finance or related field of study plus 2 years of experience in the job offered or as Model Developer, Quant 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 remediation using quantitative methods, including numerical calculus, linear interpolation, non-linear interpolation, and proxy filling; Developing scalable data lake storage solutions with integrated analytical frameworks using object-oriented design and distributed computing to extract, transform, and analyze data used for risk modeling and calculation; Enhancing core cal...