Risk Analyst [Multiple Positions Available]

JPMorganChase•Jersey City, WA

1d•Onsite

About The Position

Perform statistical data analysis to ensure accuracy of the time series data used in firmwide Value at Risk (VaR) calculation for Market Risk management. Analyze Average Daily Trading Volume (ADTV) data for data quality issues to be addressed in the process of establishing the Gross Market Concentration (GMC) limit and calculating the Strategic Stress Exposure (SSE) for Counterparty Credit Risk management. Build analytical and visualization tools for identifying trends and patterns, anomalies and spurious data including spikes, gaps, cyclicality and oscillation to improve data quality and integrity. Extract, assemble, organize and analyze large amounts of data from multiple and disparate sources by employing advanced techniques to scale up data management activities with efficiency and accuracy. Validate time series data by leveraging knowledge of financial products and risk factors in Equities, Fixed Income, FX, and Commodities, conducting research on market events, and interacting with trading desks. Collaborate with product specialists to document end-to- end data lineage for derived time series constructed according to VaR models, ensuring transparency and traceability. Monitor the ongoing performance of VaR model by analyzing and confirming movements in time series data to deliver market insights and solutions regarding data quality enhancements. Implement controls to prevent data quality issues, identify and mitigate risks, and ensure data satisfies the statistical properties required for model usage. Produce data quality metrics and KPI reports to assess and identify improvement areas for data quality and communicate findings to senior management and internal control functions. Liaise with Technology teams to onboard new data sources for data required by VaR calculators and Counterparty Credit risk limits to measure risk accurately. Provide analytical support for regulatory matters and facilitate audit processes.

Requirements

Bachelor's degree in Information Science, Applied Economics, or related field of study plus 2 years of experience in the job offered or as Risk Analyst, or related occupation.
Performing risk calculation and sensitivity analysis across Equities, Fixed Income, FX, and Commodities asset classes using Python and Excel, applying greeks, and leveraging financial product knowledge of futures, options, credit default swaps, and securitized products.
Estimating financial instrument profit and loss and conducting Value at Risk (VaR) impact analysis using VaR modeling methods, including variance-covariance, historical simulation, and Monte Carlo simulation.
Conducting backtesting and stress testing using Python including Scikit-learn and R including dplyr and ggplot2 to validate and explain significant changes in portfolio risk resulting from movements in market data.
Analyzing financial datasets using Python, SQL, and Excel and ensuring their integrity by applying statistical techniques including correlation analysis, linear regression, and outlier detection algorithms including z-score analysis and interquartile range (IQR) analysis.
Evaluating counterparty credit risk by analyzing exposure concentrations across multiple dimensions, including issuers, regions, industry sectors, and products.
Developing numerical programs for financial data analytics using Python and Python libraries including NumPy, Pandas, SciPy, Seaborn, and Matplotlib to process, model, and visualize market data.
Programming SQL operations, including multi-table joins, nested queries, and analytic functions and optimizing query performance to process and analyze datasets from multiple sources.
Performing data analysis and developing reports using Excel functions, including Index, Match, Data, Analysis add-ons, Pivot Tables, and dynamic charts.
Identifying variance drivers to explain complex changes in financial datasets and delivering actionable insights to support decision-making within risk organizations.
Creating key performance metrics by applying statistical analysis to measure the significance of data quality issues affecting risk measures.
Supporting regulatory exams by providing requested data and detailed analyses leveraging SQL and Excel Macros to extract, process, and report information.
Identifying and implementing process improvements to strengthen controls and enhance operational efficiency and data integrity.

Responsibilities

Perform statistical data analysis to ensure accuracy of the time series data used in firmwide Value at Risk (VaR) calculation for Market Risk management.
Analyze Average Daily Trading Volume (ADTV) data for data quality issues to be addressed in the process of establishing the Gross Market Concentration (GMC) limit and calculating the Strategic Stress Exposure (SSE) for Counterparty Credit Risk management.
Build analytical and visualization tools for identifying trends and patterns, anomalies and spurious data including spikes, gaps, cyclicality and oscillation to improve data quality and integrity.
Extract, assemble, organize and analyze large amounts of data from multiple and disparate sources by employing advanced techniques to scale up data management activities with efficiency and accuracy.
Validate time series data by leveraging knowledge of financial products and risk factors in Equities, Fixed Income, FX, and Commodities, conducting research on market events, and interacting with trading desks.
Collaborate with product specialists to document end-to- end data lineage for derived time series constructed according to VaR models, ensuring transparency and traceability.
Monitor the ongoing performance of VaR model by analyzing and confirming movements in time series data to deliver market insights and solutions regarding data quality enhancements.
Implement controls to prevent data quality issues, identify and mitigate risks, and ensure data satisfies the statistical properties required for model usage.
Produce data quality metrics and KPI reports to assess and identify improvement areas for data quality and communicate findings to senior management and internal control functions.
Liaise with Technology teams to onboard new data sources for data required by VaR calculators and Counterparty Credit risk limits to measure risk accurately.
Provide analytical support for regulatory matters and facilitate audit processes.