1. Drive Execution Across the Wind Engineering AI Portfolio 

• Maintain visibility across all approved AI initiatives. 

• Monitor progress against commitments, milestones, deliverables, and success metrics. 

• Ensure initiative owners remain aligned to agreed execution plans. 

• Proactively identify execution risks, delays, and resource constraints. 

• Escalate issues requiring leadership intervention. 

• Develop consistent project health and portfolio status reporting. 

2. Own Portfolio Operations and Governance 

• Maintain the master portfolio of AI initiatives. 

• Track initiatives through relevant lifecycle stages, including opportunity identification, assessment, business case development, proof of concept, pilot, production deployment, scaling, and sustainment. 

• Ensure governance requirements are completed at appropriate maturity gates. 

• Coordinate portfolio reviews. 

• Support prioritization activities led by the AI Process Transformation Lead. 

• Maintain portfolio documentation, scorecards, and reporting assets. 

3. Establish and Operate Portfolio Management Systems 

• Build and maintain portfolio dashboards and executive reporting mechanisms. 

• Create standardized project tracking and performance measurement processes. 

• Define portfolio health indicators and key performance measures. 

• Provide leaders with visibility into initiative health, resource capacity, risks, dependencies, value realization progress, and adoption metrics. 

• Enable data-driven portfolio resolutions and transparent leadership reviews. 

4. Lead Cross-Functional Program Coordination 

• Coordinate execution across Engineering, Digital, ARC Foundry, and business stakeholders. 

• Manage dependencies across initiatives and functional teams. 

• Facilitate alignment when initiatives span multiple engineering domains. 

• Identify and resolve execution bottlenecks. 

• Support resource planning and workload balancing across the portfolio. 

5. Drive Operational Excellence 

• Apply Lean and continuous improvement principles to AI portfolio operations. 

• Develop standard work for portfolio management activities. 

• Improve the efficiency of intake, governance, reporting, and delivery processes. 

• Reduce unnecessary complexity while maintaining appropriate guardrails. 

• Build repeatable execution frameworks that can scale with portfolio growth. 

• Drive Kaizen activities focused on execution effectiveness and supervision visibility. 

6. Translate Strategy into Action 

• Support the decomposition of strategic objectives into executable workstreams. 

• Help define milestones, deliverables, ownership structures, and success measures. 

• Partner with initiative owners to create realistic execution plans. 

• Ensure approved AI initiatives maintain momentum through implementation. 

• Align portfolio execution activities with broader Wind Engineering transformation objectives. 

Required Qualifications 

• Bachelor’s degree in Engineering, Computer Science, or a related technical discipline. 

• Minimum 5 years managing projects, programs or operational experience. 

•  Significant experience managing complex programs, portfolios, or PMOs within a large, matrixed organization. 

• Proven ability to manage multiple concurrent workstreams, prioritize deliverables, and drive projects through full lifecycle stages. 

•  Demonstrated success in facilitating alignment and influencing cross-functional stakeholders without direct authority. 

Preferred Qualifications 

• Experience supporting AI, software, or digital transformation portfolios within engineering, industrial, manufacturing, or energy sectors. 

• A demonstrated track record of improving processes and advancing operational maturity using frameworks like Lean, Six Sigma, Agile, Scrum, or PMP. 

• Proven ability to develop executive-level reporting and governance mechanisms, paired with excellent writing, presentation, and facilitation skills to influence stakeholders without direct authority. 

• Experience with transformation management offices, enterprise change programs, and a strong systems-thinking mindset to drive AI adoption and development lifecycles.