• Solve real business problems with data: Own end-to-end data science work, from problem framing and data exploration to modeling, validation, and production impact. • Lead high-impact initiatives: Drive complex efforts such as vendor leakage detection and prevention, delivering measurable revenue or cost improvements. • Innovate on methods and approaches: Go beyond standard analyses by developing new metrics, models, or workflows when existing approaches fall short. • Build and productionize models: Design, build, and support production ML or LLM-powered solutions in collaboration with engineering and product partners. • Work deeply with data: Use SQL fluently to explore large datasets, build reliable data assets, and validate results. • Influence cross-functionally: Partner with Product, Engineering, Finance, and Operations to align on goals, tradeoffs, and execution plans. • Set technical direction (especially at L7): Raise the bar for data science quality, scalability, and impact across the organization through mentorship, design reviews, and technical leadership.