• Lead, mentor, and develop a data team (data analysts and engineers) • Define and execute the data strategy aligned with business goals • Ensure data quality, reliability, and governance across the organization • Build and maintain scalable data models and data architecture • Drive data democratization, enabling non-technical stakeholders to access and use data • Partner with Product and Marketing teams to identify opportunities and improve performance (e.g. acquisition, retention, user behavior) • Optimize and evolve the current data stack with a business-impact mindset

• Research new techniques and frameworks for NLP tasks • Design new experiments for data abstraction and categorization of sentences/paragraphs • Work with our lawyers to review initial results and implement pre and post-processing scripts to improve the accuracy of models • Run data analysis on our dataset to design potential rules for annotation • Improve the architecture of data modeling and data tooling in partnership with Engineering and cross-functional business partners • Own your projects and use this autonomy to find creative and innovative ways of solving problems and delivering solutions.

• Partner with technical and business leaders to design key metrics, dashboards, and AI-enabled data products that are widely-adopted and influence cross-functional strategic direction and multi-year roadmaps • Establish hypothesis-driven decision-making as the default operating mode across partner teams through statistically rigorous experimentation • Tackle undefined, open-ended problems by defining both the 'what' and 'how' and conducting rigorous analyses that drive meaningful product and business impact • Transform complex findings into compelling narratives that provide strategic clarity and influence broader organizational direction • Collaborate closely with Data Engineers to influence the design, development, and maintenance of dimensional data models, ensuring data is complete, accurate, and reliably delivered • Champion world-class data governance practices, continuously raising the bar for data & code quality across technical teams • Partner with stakeholders to align roadmaps with business priorities, anticipating emerging needs before they are formally requested and driving positive outcomes in high-stakes contexts • Operate as a force multiplier whose influence fosters growth across partner teams, providing incisive technical mentorship, inspiring intellectual curiosity, and driving clarity amidst ambiguity

• Build and deploy predictive models for operational planning and execution (e.g., cycle time, throughput, labor/time estimation, service levels) • Develop and improve optimization and decision algorithms (e.g., routing, replenishment, inventory positioning, parameter estimation) • Design experiments and evaluation frameworks to validate impact (accuracy, latency, cost reduction, productivity lift) • Translate business workflows into model-ready problem statements, features, and measurable success criteria • Partner with software engineers to ensure solutions are production-ready (reliable inference, monitoring, versioning, performance tuning) • Create clear documentation and stakeholder-ready summaries of methodology, assumptions, and results • Contribute to architecture and scaling decisions for data/ML solutions (performance, robustness, and maintainability)