Lead Financial Time Series Model Developer

JPMorganChase; Jersey City (NJ); On-site; USD 160,000 - 238,000; Full time

Job summary

JPMorganChase is seeking a senior quantitative data scientist to oversee daily time series analytics, ensuring high-quality data for risk limits and VaR calculations. The role requires mentoring junior staff, developing scalable models and data infrastructure, and delivering robust analytics for multiple asset classes.

Candidates should have a Master's in Computational Finance and 2+ years of experience in model development or quantitative research, with strong Python, SQL, and time series

Qualifications

• Master's degree in Computational Finance or related field plus 2 years of experience in model development or related roles.
• Demonstrated experience with financial time series analytics and quantitative methods.

Responsibilities

• Oversee daily calculation of Average Daily Trading Volume and ensure timely delivery of high-quality data for risk limits.
• Lead implementation projects by overseeing analytical work and reviewing code from junior developers.
• Coach and mentor junior colleagues to enhance quantitative and technical skills.
• Develop and maintain advanced models and infrastructure to detect anomalies in time series data.
• Create APIs and visualization tools for time series data management and analysis.
• Design scalable data ingestion frameworks to onboard new data sources.

Skills

Python

NumPy

Pandas

SciPy

Seaborn

Matplotlib

SQL

Time series analysis

Statistical modeling

Education

Master's degree in Computational Finance

Tools

Git

Linux

Job description

JPMorganChase is seeking a senior quantitative data scientist to oversee daily time series analytics, ensuring high-quality data for risk limits and VaR calculations. The role requires mentoring junior staff, developing scalable models and data infrastructure, and delivering robust analytics for multiple asset classes.

Candidates should have a Master's in Computational Finance and 2+ years of experience in model development or quantitative research, with strong Python, SQL, and time series