• Critically evaluate ML model outputs to ensure alignment with financial theory and real-world market dynamics, while accounting for client-specific objectives, constraints, and investment frameworks. • Translate research signals into actionable investment strategies and portfolio construction frameworks. • Collaborate with ML researchers to refine models, incorporating financial domain expertise and contributing to model design where needed. • Design, prototype, and scale quantitative models using Python and Java, maintaining a high standard for code quality and modularity. • Contribute to the financial validation layer of the R&D cycle by developing and maintaining test frameworks that identify inconsistencies and support continuous model improvement. • Translate complex portfolio objectives into rigorous, testable modeling specifications that bridge the gap between investment intent and algorithmic execution. • Collaborate across technical workstreams to ensure research outputs are aligned with investment objectives and successfully integrated into production workflows.