Vinicius Ribeiro Machado da Silva, Ph.D.

Formally appointed Technical Authority Level 2 (TA2) for the Computer Sciences & Engineering discipline - Artificial Intelligence domain at Shell. As TA2, I provide independent assurance of critical AI decisions before deployment in operations, with accountability for decision quality, risk management, safety, robustness, ethics, and long-term value. As AI Portfolio Manager at Shell's Brazil Technology & Digital Hub, I define and govern the strategic roadmap for AI initiatives, aligning R&D investments with business priorities and driving projects from ideation to scalable production deployment. Key responsibilities: - Govern a multi-project AI portfolio across Shell Brazil's digital transformation agenda - Provide technical assurance on AI solutions deployed in critical energy operations - Lead cross-functional teams bridging data science, engineering, and business stakeholders - Partner with academia and industry to accelerate innovation and de-risk R&D investments - Drive end-to-end AI lifecycle: problem framing → model development → MLOps → organizational adoption

Artificial Intelligence Portfolio Manager at Shell’s Brazil Technology Digital Hub, leading the development and deployment of AI-driven solutions. I manage strategic investments in AI initiatives, driving end-to-end R&D projects, from ideation to scalable deployment, partnering with industry and academia to maximize business impact.

Led the convergence of data science and scientific software engineering at TechnipFMC, coordinating a team responsible for delivering AI-powered engineering tools for subsea and flexible pipe systems. - Defined the team's technical strategy and product roadmap, bridging domain engineering knowledge with modern ML practices - Delivered engineering automation solutions that reduced manual analysis time and improved design reliability - Introduced data-driven methodologies into workflows traditionally driven by numerical simulation - Managed stakeholder relationships across R&D, product, and operations

Served as technical lead for applied machine learning and numerical methods in the context of flexible pipe structural analysis. Designed and validated deep learning models for failure prediction and performance optimization, work that resulted in a Best Paper Award from ASME. - Pioneered the adoption of ML techniques in an engineering domain previously reliant on physics-based simulation - Published peer-reviewed research, establishing external credibility for the team's technical direction - Mentored junior engineers in data science methodologies and software engineering best practices

Developed advanced numerical methods and early-stage data science applications for subsea engineering challenges. This role laid the foundation for the AI-first approach later adopted by the team. - Built computational models for structural behavior prediction under complex loading conditions - Contributed to internal tools that improved engineering simulation accuracy and speed - Began integrating statistical and ML methods into traditionally physics-based workflows

Early-career role focused on numerical simulation and computational engineering for flexible riser systems. Developed technical depth in structural mechanics and scientific computing that became the foundation for later leadership in applied AI.

Taught undergraduate engineering courses while simultaneously leading R&D at TechnipFMC — an experience that sharpened communication skills across technical and non-technical audiences, and reinforced the ability to translate complex concepts into accessible frameworks.

Flexible pipe design

Suport on global analysis of rigid and flexible risers for new implementations in the software ANFLEX

Riser-soil interaction analysis

Applied Deep Learning to marine slender structures

Full Stack Deep Learning Bootcamp

• Data Science: From Data to Insights with MIT IDSS (Awarded 1.3 CEUs)

Academic Exchange - EUBRANEX - ERASMUS MUNDUS