• Identify and structure a problem • Formulate hypotheses • Analyze data to validate hypotheses and provide evidence for decision-making • Make decisions with calculated risks • Formulate policies for the business • Monitor experiments, implementations, and results • Scrutinize numbers and decisions, acting as a guardian of a data-driven decision culture • Communicate findings to leadership and support different business areas in strategic decisions • Work daily on a variety of topics and with people from diverse backgrounds

• Support a Centers for Disease Control and Prevention (CDC) public health evaluation initiative • Conduct public health evaluation activities through evaluation planning, implementation, data analysis, technical assistance and reporting • Develop large-scale program logic models • Facilitate internal and external partner meetings • Provide assistance or training on evaluation methods, concepts, and tools • Conduct implementation research including assessing fidelity and contextual factors

• Support public health evaluation activities through evaluation planning, implementation, data analysis, technical assistance and reporting • Conduct systematic assessment of CDC public health evaluation initiatives • Develop program logic models and facilitate internal and external partner meetings • Provide assistance or training on evaluation methods, concepts, and tools

• 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.