Seattle, Washington, United States
At AWS, my commitment to digital strategy and innovation has been instrumental in delivering state-of-the-art AI and cloud-based solutions. With a background that includes a successful tenure at CNRL, my expertise focuses on transforming complex industry trends into viable, cutting-edge products for our clients. Our team's collaborative efforts have propelled the rapid deployment of digital initiatives, leading to substantial gains in operational efficiency. The analytical skills and hands-on experience with AI and machine learning acquired during my previous role enrich my product management approach, ensuring alignment with AWS's mission to pioneer through innovation.
Achieved $6.1M annual free cash flow improvement by deploying AI-driven anomaly detection models across billing and operational infrastructure processing 2B+ records/second, enabling early identification of systemic issues across global regions. Owned multi-year product roadmap for enterprise commercial platforms by aligning AI/ML investment priorities with infrastructure-scale growth targets, balancing reliability, regulatory requirements, and customer commitments. Designed executive KPI frameworks and dashboards using large-scale transactional datasets to monitor performance, adoption, and operational risk, improving C-suite decision quality across global operations. Drove go-to-market strategy for enterprise customers with power-intensive data center footprints by translating complex technical constraints into commercial outcomes, partnering with engineering, data science, finance, and legal teams. Evaluated and integrated AI tools, APIs, and vendor platforms into production systems by establishing model monitoring, drift detection, and retraining workflows, ensuring scalability and reliability at production grade.
Delivered $6.4M+ in net benefits by developing predictive analytics and automation solutions for power, electrical, and operational systems, reducing equipment failures and unplanned shutdowns across multiple operating sites. Prevented environmental and safety incidents by designing predictive monitoring models using IoT sensor data and cloud-hosted analytics (AWS), identifying equipment failures and environmental risks before they escalated. Supported solar, battery storage, and power-adjacent pilot initiatives by providing geospatial data analysis, feasibility scoring, and performance measurement frameworks, informing go/no-go decisions on renewable energy projects. Reduced manual engineering effort by ~$500K annually by implementing computer vision and cloud-based analytics platforms to digitize infrastructure workflows, improving data quality for site planning and construction oversight. Navigated regulated energy environments by partnering with engineering, operations, and environmental teams to deploy solutions accounting for land use constraints, permitting requirements, and environmental review processes.
Reduced operating costs by ~$30M over five years by executing a $40M cloud migration program that transitioned legacy field operations and energy asset applications to scalable cloud platforms (AWS). Improved capital project delivery by building integrated financial, project, and operational systems used to plan, budget, and track multi-year energy infrastructure investments across distributed assets. Enhanced executive decision-making by developing dashboards and automated reporting to monitor budget adherence, project delivery risk, milestone tracking, and operational performance across geographically distributed sites. Modernized brownfield infrastructure data flows by redesigning reporting for existing operating facilities, improving visibility into asset performance, maintenance schedules, and regulatory compliance. Aligned cross-functional teams by partnering with engineering, operations, and finance stakeholders to coordinate project schedules, cost controls, and milestone tracking across multiple development programs.