Data Engineering:
Design, build, and maintain scalable data pipelines using Azure and other cloud-based tools to ingest, clean, and process large datasets from multiple sources (market data, accounting systems, analyst models).
Ensure data integrity and availability for investment analysis and reporting.
Data Modeling & Analytics:
Develop and implement models to assess credit risk, forecast asset pricing, and analyze fixed income securities.
Use Python for statistical analysis, predictive modeling, and automation of analytical workflows.
Reporting & Visualization:
Generate and automate reports that provide insights into portfolio performance, risk exposure, and market conditions.
Collaborate with portfolio managers to deliver actionable data-driven insights through clear visualizations and dashboards.
Collaboration & Stakeholder Engagement:
Work closely with investment teams to translate complex data analysis into investment strategies and risk management solutions.
Communicate technical results to non-technical stakeholders, ensuring transparency and clarity.
Technical Expertise:
Proficiency in Python for data analysis, modeling, and automation.
Strong knowledge of Azure (data storage, processing services) and SQL databases for data management.
Experience with data modeling, including building and deploying predictive models.
Analytical & Problem-Solving Skills:
Ability to interpret large datasets and transform them into actionable investment insights.
Strong understanding of fixed income markets and credit instruments.
Bachelor’s or Master’s in Data Science, Computer Science, Finance, or a related field.
Experience in real estate, fixed income or private credit investing is a plus.
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