Description

We are transforming musculoskeletal healthcare through intelligent, scalable data and technology solutions. Our Enterprise Data & Analytics teams empower everyone across HSS – including clinicians, researchers, financial and operational teams – with timely, trusted insights, intelligent automation, and technology-enabled workflows that enhance patient care and operational performance. We operate in cross-functional Product-Oriented Delivery (POD) teams, leveraging modern platforms to turn data, AI, and technology into action. 

We are seeking a Director, Data Enablement and Automation to help advance how HSS uses data, AI, and automation across the organization. In this role, you will help translate emerging technologies into scalable solutions, enable adoption of data and products, and embed guidance and support directly into workflows. You will work across Technology teams and with business stakeholders to connect tech, people, and processes – driving measurable improvements in how teams use data, automation, and AI to deliver impact.

Core Responsibilities 

• Drive Data Enablement & Automation Strategy: Define and advance a scalable strategy for data enablement, automation, AI adoption, and workforce capability across HSS Data, translating emerging technologies into practical solutions that improve how teams work and make decisions. 

• Drive Product-Led Enablement: Treat workforce capability and learning as a scalable product, designing iterative, metric-driven enablement experiences rather than one-off training. Ensure adoption and user enablement are built into data, AI, and technology products from the outset. 

• Product Team Collaboration: Partner directly with internal data science, AI, and product engineering teams to integrate user onboarding, contextual guidance, and adoption frameworks into newly launched AI agents and data platforms. 

• Identify and Operationalize Emerging Technology: Identify emerging generative AI, automation, agentic AI, and data technologies at the edge; evaluate their potential; translate them into concrete business workflows; and design scalable interventions that drive adoption across the enterprise. 

• Build AI-Enabled Workflows: Develop and architect orchestrations that integrate foundation models, AI agents, automation, and data services, ensuring effective interfaces between AI systems and automated processes. 

• Automate Complex Business Processes: Identify, design, and implement automation solutions supporting strategic domains 

• Embed Enablement in the Flow of Work: Architect in-the-flow-of-work learning systems, contextual guidance, prompt libraries, agentic assistants, and other support mechanisms that help employees navigate complex tasks and effectively use new data, AI, and automation capabilities in real time. 

• Drive Cross-Functional Product Enablement: Integrate user onboarding, contextual guidance, adoption frameworks, and feedback mechanisms into newly launched AI agents and data products. 

• Develop and Integrate Technical Solutions: Build, monitor, and enhance ETL processes, API integrations, workflow automations, and other technical solutions spanning multiple data sources, platforms, and business functions. 

• Measure Adoption and Business Impact: Define and track quantitative adoption and performance metrics, such as active tool usage, prompt effectiveness, workflow completion, task cycle time, and automation utilization, to connect enablement and automation efforts directly to measurable business outcomes. 

• Establish Continuous Feedback Loops: Instrument feedback mechanisms across data and AI product experiences to capture user friction points, behavioral signals, and qualitative feedback, using these insights to inform product roadmaps, enablement strategies, and iterative improvements. 

• Champion Sustainable Solutions: Document, test, monitor, and deploy solutions with a focus on scalability, maintainability, usability, and long-term adoption by the broader organization. 

• Ensure Responsible Deployment: Ensure data, automation, and AI solutions meet high standards for security, compliance, privacy, and governance appropriate for a healthcare environment. 

• Build Organizational Capability: Foster a culture of learning, experimentation, and continuous improvement. Mentor teammates, share discoveries, develop best practices, and help grow HSS Data’s capabilities in automation, AI, and data enablement. 

• Stay Ahead of Technology: Continuously evaluate advances in generative AI, agentic AI, machine learning, automation, analytics, and data platforms, advocating for approaches that expand HSS Data’s ability to deliver scalable, innovative solutions. 

Experience & Qualifications 

• 7+ years of experience designing and implementing automation, data, AI, technology enablement, or related solutions across multiple technologies and business functions.  

• AI & Data Literacy: Deep hands-on familiarity with modern generative AI capabilities, LLM tooling, prompt design, agentic workflows, automation technologies, and data product lifecycles. Demonstrated experience translating emerging technologies into practical business workflows and scalable solutions.  

• Technical Acumen: Proficiency with modern automation, integration, API, cloud, and data platform technologies, with the ability to understand and contribute to technical solution design and development.  

• Product-Led Enablement & Adoption: Demonstrated experience designing scalable approaches to technology adoption and workforce enablement, with the ability to bridge technical and non-technical stakeholders and translate business needs into effective solutions.  

• Data-Driven Evaluation & Human-Centered Design: Experience using product analytics, adoption funnels, workforce telemetry, and feedback mechanisms to evaluate effectiveness and inform iteration. Ability to apply UX research, rapid prototyping, journey mapping, performance consulting, or similar approaches to enterprise technology or workforce transformation initiatives.  

• Healthcare Experience: Experience with healthcare data, or similarly complex and regulated domains preferred.  

• Demonstrated commitment to continuous learning, experimentation, and measurable impact is key. 

Key Competencies 

• Strategic and Product-Oriented: Able to translate organizational priorities and emerging technology into scalable products, workflows, and enablement strategies. 

• Technical and Business Acumen: Comfortable operating across technical architecture, data, AI, automation, and business process design while translating complex concepts for diverse audiences. 

• Data-Driven: Uses quantitative and qualitative evidence to understand adoption, measure impact, identify friction, and continuously improve solutions. 

• Human-Centered: Understands how people work and learn, and designs experiences that make technology intuitive, useful, and embedded in day-to-day workflows. 

• Cross-Functional Influencer: Proven track record of bridging the gap between technical engineering teams (data engineers, AI builders) and non-technical business units to drive tool adoption. 

• Creative and Analytical: Strong analytical, critical, and creative thinking skills with the ability to turn ambiguous problems into practical, scalable solutions. 

• Change Leader: Motivated self-starter who embraces experimentation, champions adoption, supports team growth, and helps shape the future of how HSS engages with actionable data. 

• Responsible and Sustainable: Committed to delivering secure, robust, governed, and sustainable solutions appropriate for a healthcare environment.