Top Tech Jobs & Startup Jobs

Leads Data and Digital Account Managers serving hospital and health system portfolios. Owns revenue retention, renewals, client satisfaction, account expansion, executive relationships, forecasting, and CRM discipline. Partners with Sales, Product, Marketing, Service Delivery, and enterprise leadership to execute commercial strategies, communicate customer insights, and improve client outcomes. Develops leaders, drives accountability, and supports organizational priorities and sustainable growth.

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

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.

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.

Develop and deploy scalable data science products using machine learning, generative AI, NLP, statistical modeling, and predictive analytics. Analyze complex datasets, manage end-to-end modeling workflows, communicate insights through dashboards and presentations, and partner with engineering teams on production deployment. Provide technical guidance, apply responsible AI and model governance practices, and evaluate emerging technologies for business and clinical outcomes.

Leads the design, development, and deployment of advanced data science, machine learning, generative AI, and agentic AI solutions. Builds scalable analytics products, RAG systems, and evaluation frameworks; establishes technical standards and responsible AI governance; mentors data scientists; and partners with engineering teams to deploy and monitor production solutions. Translates complex analyses into actionable clinical, operational, and economic insights while guiding technical strategy and client-focused initiatives.