Senior Quant Analyst, Quantitative Developer

LSEG•New York, NY

About The Position

Role Profile The role is in Quantitative Data Research team which sits within Analytics Research Group. This is a senior level quantitative developer role responsible for application development, including the responsibilities for running and refactoring legacy processes as well as coordinating the work of the other developers. This individual will work closely with colleagues within ISD Research and Product Management as well as others with ISD Data Operations, and Technology. Key Responsibilities • Design, develop, and maintain quantitative data processing pipelines to support research, analytics, and production environments. Commit to support and improvement of existing implementations • Build and optimize data workflows for large-scale financial datasets, ensuring efficiency, scalability, and data integrity across ingestion, transformation, and delivery layers. Understand data flows that support quant models. • Work within AWS and Azure cloud environments, leveraging services such as S3, Lambda, EC2, Databricks, and Azure Data Factory to support distributed computation and automation. • Collaborate closely with quantitative analysts, data scientists, and business stakeholders to translate modeling requirements into robust, production-ready systems. • Implement best practices for code versioning, testing, and CI/CD, ensuring maintainable and reproducible quantitative infrastructure. • Monitor and troubleshoot pipeline performance, proactively addressing bottlenecks and optimizing for speed, cost, and reliability. • Continuously acquire new technical skills—including modern data frameworks, cloud tools, and programming techniques—to enhance the team’s analytical capabilities. • Engage in active learning of the business domain, developing a deep understanding of financial products, market data, and risk factors to better align technical solutions with analytical needs. • Contribute to documentation, peer reviews, and knowledge sharing to support continuous improvement within the quantitative development team. Key Behaviours Integrity • Has the sustained drive and energy to deliver support service to time and quality. Innovation and Partnership • Open to and willingly adopts new processes / approaches / ways of working. • Seeks information/inputs from colleagues/clients. • Deals with conflict successfully. Excellence • Willingly puts in the effort to ensure activities completed on time and to the quality required. • Pro-active and demonstrates initiative. • Prioritises activities according to business and operational need. • Analysis and problem-solving skills. • Analyses issues to identify the most appropriate solutions. • Utilises all available resources and toolsets to investigate and resolve problems.

Requirements

Strong working knowledge of Python programming language is required.
Experience with data wrangling and creating data pipelines in Python is strongly desired.
Technical degree in a field such as Physics, Computer Science, Engineering, Statistics or Mathematics preferred.
Strong leadership skills, desire, and ability to innovate.

Nice To Haves

Familiarity with programming, computing and searching algorithms is a plus.
Familiarity with Linux environment is a plus.
Familiarity with PySpark applications is a plus.
Familiarity with ETL technologies a plus.
Familiarity with ML and AI technologies and their applications is a plus
Familiarity with cloud-based platform like AWS, Azure, DataBricks and Snowflake is a plus
Familiarity with finance and fixed income a plus.

Responsibilities

Design, develop, and maintain quantitative data processing pipelines to support research, analytics, and production environments. Commit to support and improvement of existing implementations
Build and optimize data workflows for large-scale financial datasets, ensuring efficiency, scalability, and data integrity across ingestion, transformation, and delivery layers. Understand data flows that support quant models.
Work within AWS and Azure cloud environments, leveraging services such as S3, Lambda, EC2, Databricks, and Azure Data Factory to support distributed computation and automation.
Collaborate closely with quantitative analysts, data scientists, and business stakeholders to translate modeling requirements into robust, production-ready systems.
Implement best practices for code versioning, testing, and CI/CD, ensuring maintainable and reproducible quantitative infrastructure.
Monitor and troubleshoot pipeline performance, proactively addressing bottlenecks and optimizing for speed, cost, and reliability.
Continuously acquire new technical skills—including modern data frameworks, cloud tools, and programming techniques—to enhance the team’s analytical capabilities.
Engage in active learning of the business domain, developing a deep understanding of financial products, market data, and risk factors to better align technical solutions with analytical needs.
Contribute to documentation, peer reviews, and knowledge sharing to support continuous improvement within the quantitative development team.