Shiv Kumar Sambasivan

• Leading the global deployment of a flagship enterprise Gen AI analytics platform across large-scale supply chain operations, driving significant annual EBIT impact. • Directing a 50-person global team across Product, Data Science, and Engineering — securing alignment across Senior VPs and business lines to champion multi-million-dollar Gen AI investments. • Defining end-to-end technology stack and architecture while translating complex technical trade-offs into high-stakes investment decisions. • Establishing rigorous engineering standards and cultivating a performance-driven culture by mentoring senior technical professionals.

* Engaged as a learning facilitator for advanced graduate level Machine Learning course, spanning a range of topics from computational learning theory to statistical modeling and programming.

• Generated $70M+ in incremental revenue by directing the architecture and deployment of enterprise-scale AIML solutions that optimized inventory management, reduced stockouts, and centralized demand forecasting. • Redesigned Nike's MLOps capabilities through next-generation frameworks that enabled model deployment & monitoring with real-time insights and operational resilience. • Restructured engineering teams using a "pods within squads" model, improving engineering throughput and cross-functional agility. • Established Nike’s Gen AI stewardship program—defining governance, driving adoption, and launching an internal innovation initiative. • Developed high-potential AIML talent across global teams, fostering a high-performance, innovation-centric engineering culture.

• Transformed data-driven decision making across multi-billion-dollar assets by directing global teams of senior engineers & geoscientists, significantly enhancing production & capital efficiency. • Delivered $250M in Net Present Value (NPV) uplift by integrating high-fidelity production time series data into investment analytics and forecasting workflows. • Reduced capital allocation by ~$150M through a predictive analytics engine; seamlessly integrated ML workflows to shape investment strategy. • Authored a comprehensive 120-page playbook on integrated field development, widely adopted as a reference for strategic capital deployment & subsurface planning. • Established an offshore analytics center of excellence from concept to full operational capability, ensuring continuous innovation and round-the-clock decision support. • Led a multi-million-dollar deep-water reservoir program, consistently exceeding long-term production and volume forecasts through data-informed strategic frameworks.

Led foundational research and development efforts in high-performance scientific computing and data-driven modeling for national security applications. * Designed the 3D Reconnection-Arbitrary Lagrangian-Eulerian (ReALE) framework, a C++/MPI-based parallel solution for simulating complex fluid-structure interactions in munitions-target scenarios. * Developed a hybrid numerical method leveraging k-means clustering to reconstruct 3D mesh grids while preserving conservation laws. * Contributed original remapping algorithms for high-order tensor reconstructions, significantly improving stability and accuracy in large-scale simulations.

Doctoral research in computational physics and multiphase flow modeling. * Developed a level-set-based Fortran hydrocode using adaptive Cartesian grids to simulate multi-material interactions under extreme loading (e.g., shock waves, penetration events). * Designed a new immersed boundary method and adaptive mesh refinement (AMR) strategy to enforce accurate boundary conditions across fractured interfaces. * Published and presented research cited in high-impact journals and national laboratories.