Pleasanton, California, United States
Enterprise data and analytics leader with 20 years of experience defining and executing data strategy, building scalable analytics platforms, and driving AI-led transformation. Proven track record of delivering measurable business impact and enabling executive decision-making.
Main resposibilities • Defined and executed enterprise data strategy aligned with corporate objectives • Architected centralized data platform (Databricks) enabling scalable analytics and governance • Built open data architecture improving cross-functional data access and reuse • Established KPI frameworks tracking performance, test progress, and operational readiness • Delivered 10x–300x efficiency gains through automation and scalable data systems • Partnered with leadership on program and resource planning, improving prioritization and allocation • Developed analytics solutions reducing manual reporting and accelerating decision cycles • Defined metrics for productivity, quality, and cycle time enabling process improvements Cross-Functional Analytics Applications • Delivered analytics solutions supporting program management, operations, and engineering • Integrated data across systems into unified reporting and analytics tools • Supported use cases in resource planning, workforce insights, and cost visibility GenAI & AI Transformation • Deployed GenAI tools automating knowledge retrieval and workflow execution • Enabled natural language access to enterprise data for non-technical users Leadership • Built and scaled global team of 15 data scientists and engineers • Influenced data strategy, platform investments, and analytics roadmap with senior leadership
Supporting Corporate Engineering, Technology and Operations (CETO) and the LM Executive Champion for Penn State in all activities involving university engagement, talent recruitment, research collaborations, etc.
Application of Machine Learning and Deep Learning techniques to a variety of aerospace-related challenges - Deep Learning applied to helicopter Flight Test (time-series) data - Machine Learning applied to helicopter maintenance data - IoT Predictive Manufacturing applied to shop floor data to predict discrepant products - Prognostics and Health Monitoring to extend design life of critical components (prognostics & reusability) - Usage Based Lifing using Machine Learning to predict helicopter parts removals (sustainment)
Aero/Thermo Engineer, Dynamic Systems - Rotors Structures Production support - S-61T certification: static substantiation - CH-148 Canadian Maritime Helicopter: development support for blade retention joint - S-333M Certification: MR and TR fatigue analysis, DNEs development, Ground and Flight Test support, scheduling & budget estimating, EV tracking - Field and AOG support for S-76, S-92, S-300 - India Navy SH-3H: certification proposal, scheduling & estimating - TUHP Engineering Training for TAI: course preparation, Rotors team lead, scheduling & estimating - S-97: Flight Test support Research and innovations support - High Speed Rotor Test Technology (HSRTT): rotor head analysis, Wind Tunnel support - DARPA VTOL X-Plane: rotor head static and fatigue analysis - X2 IBC: rotor head static and fatigue analysis
DARPA STO “Structural Logic” program (team with Purdue Univ., Boeing and NextGen Aeronautics Inc.) - Analysis, design and testing of several devices for vibration and shock absorption exploiting innovative non-linear dynamics for combined high stiffness and high damping - Analyst, team lead and admin (EV tracking) roles Project lead for the AFOSR “Bi-stable cellular honeycombs” project - Static and dynamic investigation and testing of novel bi-stable structures (e.g., arches, von-Mises truss) - Analysis, design and testing (whirl stand) of a novel rotor blade prototype capable of variable chord
Taught the course "Introduction to Engineering Design" for sophomores
Research activities within the DARPA STO funded “Structural Logic” project, sub-contracted from NextGen Aeronautics