01Key Responsibilities
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
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