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The internship supports the Indirect Spend team through supplier engagement, data analysis, and project execution, requiring interaction with various teams and leadership.

This role involves collaborating with business and technology teams to optimize solutions within Microsoft Dynamics 365, analyzing business requirements, and supporting Agile delivery processes to enhance operational efficiency and user experience.

The Director will drive organizational transformation through change management and design, enhancing effectiveness and performance while fostering a culture of engagement and collaboration across the organization.

Manage day-to-day engineering delivery for AI Intelligent Automation initiatives, leading one or more Delivery PODs through the AI Delivery Lifecycle. Oversee Agile execution, Azure DevOps planning, sprint management, release readiness, production support coordination, delivery reporting, KPI tracking, risk mitigation, and continuous improvement to ensure predictable, high-quality delivery of enterprise automation solutions.

Leads advanced analytics, business intelligence, reporting automation, and data modeling for spend management and purchasing performance. Analyzes purchasing, supplier, contract, and operational data; develops KPIs, forecasting models, dashboards, reports, and scorecards; and delivers actionable insights to clients and leaders. Partners across business, clinical, operational, and technical teams while improving data quality, automating reporting, managing analytical deliverables, and providing documentation and training.

Designs and executes AI Quality Engineering strategies for enterprise AI applications, LLM solutions, intelligent automation, and agentic systems. Builds validation processes, evaluation frameworks, automated testing, runtime quality controls, observability, and reliability practices. Partners with engineering, clinical, security, governance, data, and product teams to validate healthcare workflows and responsible AI deployment. Coordinates Agile testing and release activities, supports production reliability, researches emerging technologies, and mentors Quality Engineers.

Leads commercial IT strategy, capability ownership, product owner teams, governance, roadmap prioritization, investment planning, and cross-domain initiatives. Builds executive and business partnerships, aligns technology roadmaps with enterprise value, and ensures platform health, delivery readiness, adoption, and sustainability. Oversees CRM, sales enablement, pricing, customer service, marketing automation, community platforms, and related digital commercial capabilities.

Develop and maintain enterprise ontologies, vocabularies, semantic models, and knowledge graphs. Translate business use cases into semantic requirements, support graph and triplestore integration, develop SPARQL queries, and perform validation and quality controls. Manage semantic asset versioning, testing, documentation, and releases while partnering with data engineering, product, analytics, AI, and business teams. Apply ontology standards and software engineering practices to enable interoperable data, advanced analytics, data products, and AI solutions.

Develop automated analytics products and advanced data science solutions using machine learning, NLP, statistical modeling, predictive analytics, and generative AI. Lead end-to-end workflows from data preparation and experimentation through model validation, deployment, and visualization. Partner with product, engineering, and platform teams to move solutions from research into production, communicate findings to technical and non-technical audiences, and guide other data scientists.

Lead strategy, architecture, and execution of enterprise IAM platforms for SaaS products. Define multi-year roadmaps, modernize authorization (ReBAC/ABAC/Policy-as-Code), support human and non-human identities, oversee architecture and operations, mentor engineering leaders, drive AI-enabled engineering practices, and align IAM capabilities with security and business priorities.