Position Summary
The Director of AI, Machine Learning, and Data Architecture will lead enterprise-wide AI transformation to accelerate revenue growth, improve operational efficiency, and modernize the company's data and analytics platform. This leadership role defines AI strategy, builds high-performing teams, and delivers measurable business outcomes through advanced machine learning, agentic automation, and modernization of the enterprise data platform built on Microsoft Fabric.
The Director will oversee internal teams and external consultants—including Data Scientists, Data Engineers, Data Architects, ML Engineers, Automation Specialists, and Program Managers—while partnering with executive leadership to embed AI into core business strategy and ensure disciplined delivery of large-scale transformation initiatives.
Enterprise AI Strategy
- Define and execute a multi-year AI roadmap aligned with corporate strategy.
- Manage AI product lifecycle: define AI “products” (e.g., pricing engine, forecasting models, agentic operations assistants), own roadmaps, and align them with business priorities.
- Identify high-impact opportunities for:
- Revenue growth through predictive analytics and customer intelligence
- Pricing optimization and demand forecasting
- Agentic automation for operational efficiency
- Establish measurable KPIs tied to revenue lift, cost savings, and productivity improvements; align incentives and budgets to AI adoption and outcomes.
- Present AI strategy, ROI, and risk posture to executive leadership.
- Ensure data privacy, security, and vendor/model risk management.
- Drive repeatable delivery patterns from POC → pilot → scaled deployment with SLAs, SLOs, and lifecycle management.
Machine Learning & Advanced Analytics
- Lead development and deployment of production-grade ML models.
- Establish scalable MLOps practices for model lifecycle management, monitoring, and governance.
- Implement experimentation and A/B testing frameworks.
- Ensure responsible AI, regulatory compliance, and enterprise data governance.
Agentic Automation & Operational Efficiency
- Identify business processes suitable for AI-driven automation.
- Deploy intelligent agents integrated with ERP, HR, CRM, and operational systems.
- Reduce manual effort and improve quality and cycle time through automation.
- Measure and report ROI of automation initiatives.
Enterprise Data Platform & Microsoft Fabric Modernization
- Lead modernization of enterprise data architecture using Microsoft Fabric as the core analytics platform.
- Define and implement enterprise data strategy including:
- Lakehouse architecture and OneLake consolidation
- Real-time streaming pipelines and analytics
- Data governance, lineage, and cataloging
- Semantic modeling and standardized KPIs
- DataOps and MLOps integration
- Analytics and BI modernization
- Develop migration roadmap from legacy warehouses, ETL, and reporting tools into Fabric with minimal business disruption.
- Establish reference implementations showing end-to-end workflows from ingestion → lakehouse → semantic model → ML/AIOps → dashboards/agents.
- Create Fabric Center of Excellence to define standards, reusable data products, and best practices.
- Implement cost optimization and FinOps practices for Fabric workloads.
- Partner with IT Ops and Security to ensure scalability, reliability, and compliance.
- Improve enterprise data quality, accessibility, and governance.
Program & Project Leadership
- Establish enterprise AI and data modernization program governance.
- Manage large-scale cross-functional initiatives with structured delivery methods (Agile, hybrid, or SAFe).
- Define project charters, milestones, budgets, KPIs, and risk management plans.
- Create portfolio prioritization framework based on ROI and strategic impact.
- Lead steering committees with CIO and business leaders.
- Ensure projects are delivered on time, on budget, and with measurable outcomes.
- Manage consultants and vendors against SOWs and performance metrics.
- Build dashboards tracking delivery status, ROI, and adoption.
- Lead change management programs including training, rollout planning, and adoption tracking.
Leadership & Organizational Development
- Lead teams of Data Scientists, ML Engineers, Data Engineers, Data Architects, Automation Engineers, and Program Managers.
- Establish an inaugural platform team responsible for standards, tooling, enablement, and internal support.
- Develop hiring strategy, succession planning, and career path frameworks.
- Manage vendor/platform strategy and strategic partnerships.
- Establish AI Centers of Excellence.
- Drive adoption through training, playbooks, and embedded AI champions.
- Foster a culture of experimentation with governance for A/B testing and sandbox environments.
- Lead enterprise upskilling programs for technical teams and business users.
Stakeholder Engagement
- Collaborate with Operations, Marketing, and IT leadership.
- Translate complex AI concepts into clear business value.
- Act as interpreter between AI teams and business process owners.
- Present AI initiatives to executive leadership and boards in clear narratives tied to value, risk, and competitiveness.
- Foster a data-driven culture across the organization.
Required Qualifications
- Bachelor's degree in Computer Science, Data Science, AI, Engineering, or related field (Master's/PhD preferred).
- 10-15+ years in AI, machine learning, or advanced analytics.
- 5-8+ years leading enterprise-scale teams and transformation programs.
- Proven track record delivering AI initiatives that increased revenue or reduced costs.
- Experience modernizing enterprise data platforms, preferably including Microsoft Fabric or similar lakehouse architecture.
- Strong knowledge of:
- Machine learning and deep learning
- Data architecture and engineering
- MLOps/DataOps
- Cloud infrastructure
- Automation platforms and agentic workflows
Preferred Qualifications
- Experience leading Microsoft Fabric or similar enterprise data modernization.
- Formal project/program management experience (PMP, Agile certification, or equivalent).
- Experience presenting to executive leadership or boards.
- Experience with generative AI and large language models.
- MBA or executive leadership training.