• Own and evolve the Data Science strategy across multiple product areas, translating company and product objectives into a clear, prioritized roadmap with measurable business impact. • Drive portfolio-level prioritization, balancing near-term delivery with longer-term platform and capability investments. • Set technical direction and standards for production ML systems, with end-to-end ownership from problem framing and data strategy through deployment, monitoring, and iteration. • Partner closely with Product, Engineering, Clinical, and Operations leaders to embed data science into product workflows and decision-making. • Represent Data Science in product planning and executive forums, communicating tradeoffs, risks, and impact to senior stakeholders. • Lead and scale the Data Science organization, including managers, senior ICs, and emerging leaders, with clear expectations and ownership. • Own hiring, onboarding, and talent development, building durable career paths and a high-performing organization. • Champion responsible, interpretable, and clinically sound AI, ensuring models are appropriate for healthcare use and real-world deployment.

• Apply modeling and machine learning techniques to business problems at the intersection of product and data science • Autonomously identify and pursue research with significant business impact, and make compelling cases for prioritization and resource allocation • Diagnose data and model assumptions, curating and testing appropriate models for each dataset and business objective • Identify, compute and validate the appropriate metrics to measure success • Deliver well-documented datasets, tools, and reports to key technical and business partners • Serve as a strategic thought partner to product managers and business stakeholders, directly influencing product direction and improving user experience • Cultivate strong partnerships with cross-functional stakeholders from product, engineering, operations, design, consumer research, etc • Effectively communicate findings and insights to both technical and non-technical audiences, driving adoption and understanding of ML-driven solutions.

• Build and ship classical ML and GenAI pipelines that surface, rank, and explain candidate billing error, audit, and fraud concepts across large-scale healthcare claims data • Advance state-of-the-art research in anomaly detection, information retrieval, and weak supervision, turning research into robust production systems • Accelerate concept generation and concept validation with SMEs by designing human-in-the-loop workflows that enable high-throughput review with strong precision, recall, and traceability • Own end-to-end delivery from problem framing and data strategy to model development, evaluation, deployment, and monitoring, with a focus on reliability and customer outcomes • Partner cross-functionally with Product, Data Engineering, Operations, SMEs, and business leaders to prioritize work, unblock execution, and unlock material financial value • Help scale the team and platform as Machinify grows aggressively toward resale and IPO readiness

• Manage a team of data scientists • Direct the evaluation of trends in project demand • Cultivate and manage major long-term vendor partnerships • Lead the development of relevant insights • Collaborate with analytics teams across Kohl’s • Deploy analytics and/or product management best practices • Create clear connections to the goals of the department • Participate in industry and company-wide analytics forums