Lead Quant Developer – Time Series & Data Quality

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

Job summary

JPMorgan Chase & Co. in Jersey City, NJ, seeks a seasoned quantitative data engineer to oversee daily calculation of Average Daily Trading Volume and ensure timely delivery of high‑quality data for risk limits.

You will lead analytics projects, review code, mentor junior developers, and develop scalable models, APIs and tools for time series data, supporting VaR and multi‑asset risk calculations across Equities, Fixed Income, FX, Commodities and structured products.

Qualifications

• Master's degree in Computational Finance or related field and 2 years' experience in model development or quant research.
• Proficiency with Python and libraries (NumPy, Pandas, SciPy, Seaborn, Matplotlib) for time series analytics.
• Experience building and optimizing SQL queries and scalable data pipelines.

Responsibilities

• Oversee daily calculation of Average Daily Trading Volume and address analytical issues to ensure 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.

Skills

Python

NumPy

Pandas

SciPy

Seaborn

Matplotlib

SQL

Time series analytics

Statistical modeling

Education

Master's degree in Computational Finance

Tools

Distributed computing

Data lake storage

SQL queries

Job description

JPMorgan Chase & Co. in Jersey City, NJ, seeks a seasoned quantitative data engineer to oversee daily calculation of Average Daily Trading Volume and ensure timely delivery of high‑quality data for risk limits.

You will lead analytics projects, review code, mentor junior developers, and develop scalable models, APIs and tools for time series data, supporting VaR and multi‑asset risk calculations across Equities, Fixed Income, FX, Commodities and structured products.