Apply knowledge and take a lead role in implementing quantitative investment research products within the designated business area to support organizational goals and objectives, including gathering and collecting time series financial data, and developing quantitative models, analytics, and tests  

Develop control frameworks required to deploy and manage quant models in production environments. 

Lead data engineering implementation efforts for data processing pipelines that curate financial datasets into representations fit for model research and production applications. 

Co-develop production code in conjunction with quant researchers to deliver performant software (supported by comprehensive test coverage) that meet investment objectives while minimizing the risk of model errors; manage SDLC processes to enable CI/CD. 

Build data visualizations and other tooling to enable model researchers to efficiently monitor model performance and stability, as well as to interpret model outputs. 
 

Required Qualifications:
 

Degree in computer science, information systems, math, engineering, or other technical field, or equivalent experience 

Three plus years of experience with Python or Java 

Proficiency in one or more programming languages used for quantitative investment model development and analysis (e.g Python or R) 

Experience building data pipelines and related processes with large financial research timeseries datasets 

Proficiency in designing quant-research optimized data models that support efficient data retrieval and aggregation of financial datasets, including complex hierarchical structures 

Familiarity in developing distributed data processing and streaming frameworks and architectures 

Experience leveraging continuous integration/development tools (e.g. Jenkins, Docker, Containers, OpenShift, Kubernetes, and container automation) in a CI/CD pipeline 

Refines data analysis approaches to support informed business decision-making 
Enhances processes to ensure legal and ethical data compliance 

Innovates solutions to enhance data visualization clarity and utility 

Develops continuous monitoring mechanisms to ensure compliance and optimize performance of quantitative models in production 

Utilizes statistical techniques and hypothesis testing to analyze large datasets to support decision-making 

Leverages expertise in quantitative systems architecture to enhance the business impact of software solutions 

Optimizes software development testing and verification to enhance component reliability and address operational problems