Sr.Manager - Python Developer

Key Responsibilities

• ​ ​ • Design, implement and maintain software solutions that enable quantitative research • 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 • Apply knowledge and take a lead role in implementing quantitative systems architecture within the designated business area to support organizational goals and objectives, including designing and maintaining financial research data systems that leverage distributed/cloud computing, data analytics, and machine learning. • 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. • Validate model outputs and defines controls; document model hypotheses including goals, back-testing, and stress-testing strategies; support model optimization analysis. • Design, develop, test, and deploy systematic analytics pipelines using machine learning / statistical analysis frameworks. • Build data visualizations and other tooling to enable model researchers to efficiently monitor model performance and stability, as well as to interpret model outputs. • Assist in selection and integration of data related tools, frameworks, and applications required to expand platform capabilities. • Lead or participate in developing proof-of-concepts to understand the appropriateness of new methodologies and/or technologies for our platforms. • Analyze business problems and processes; help refine requirements and design solutions for them. • Be a champion of new ways of collaborating with technology and business partners • Influence and implement improvements and efficiencies in both the technical and non-technical aspects of the development process ​.
• Required Qualifications:
• Seven plus years of experience with Python or Java in complex operating environments • Proficiency in one or more programming languages used for quantitative investment model development and analysis ( e.g Python or R) • Experience designing, building, and supporting data pipelines and related processes with large financial research timeseries datasets, including implementing robust exception-handling, observability, and quality control capabilities • 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 • Experience operating in a regulated environment with a focus on delivering systems that are compliant with legal and ethical data standards • Familiarity with incorporating statistical techniques and hypothesis testing into the analysis of large datasets to support decision-making • Expertise in quantitative systems architecture to enhance the business impact of software solutions • Experience implementing software development testing and verification capabilities to enhance component reliability and address operational risks • Understanding of investment management principles and concepts; strong interest in becoming a domain expert • Practical experience in leveraging Generative AI capabilities to enhance productivity and efficiency, with sufficient experience to understand the pitfalls of blindly relying on such technologies