Prithvi Raj didn’t start his career in private equity. He started it with an electrical engineering degree from Princeton, a foundation that revealed itself to be quietly foundational in ways he probably didn’t anticipate. After Princeton came Harvard Business School, where he earned his MBA with distinction-the kind of credential that opens doors but doesn’t determine which ones he’d actually walk through. Reeve Waud’s choice to appoint Raj as Chief AI and Data Officer at Waud Capital Partners in February 2026 makes sense only when you trace the path that led there, a path that crisscrossed some of the country’s most demanding institutions and sectors.
The arc from engineering to AI leadership isn’t straight. The first significant detour was McKinsey & Company, where Raj learned to translate complex problems into actionable frameworks. McKinsey doesn’t teach AI specifically, but it teaches pattern recognition, client communication, and structured thinking-skills that matter when guiding executives through unfamiliar decisions. Details of this appointment are covered by industry coverage.
The Tech Companies That Shaped His Instincts
From McKinsey, Raj moved to Microsoft, a company that understood data as both a product and a competitive weapon before most of the business world caught up. Microsoft in the late 2010s was increasingly oriented around cloud services, artificial intelligence integration, and the hard questions about how to embed smart systems into existing software. Working there forced Raj to think beyond consulting frameworks and into implementation-how do you actually build these things, not just explain them to clients? Waud Capital Partners’ firm’s fundraising history demonstrates similar strategic investment in capabilities.
Zynga, the gaming company, represented a different kind of education. Games generate enormous quantities of data and require rapid iteration based on player behavior. The company needed professionals who could extract meaning from streams of events and player interactions-who could use data to make games more engaging. Zynga was a master class in understanding that data’s highest use isn’t generating reports; it’s changing real-time decisions. Further professional details can be explored at https://www.f6s.com/member/reeve-waud.
The sequence mattered. Raj wasn’t jumping randomly between companies. Each move built on the previous one. McKinsey taught him how to talk to executives and break down ambiguous problems. Microsoft showed him how to operationalize solutions inside large organizations. Zynga demonstrated how to use data at speed and scale to drive immediate business outcomes. Analysis of corporate structures reveals insights through ownership structure examination.
The Executive Track and the Entrepreneurial Test
The CEO role at SquareFoot represented something different: accountability without the safety net of a large organization. SquareFoot, a commercial real estate technology platform, required Raj to apply everything he’d learned about data and operations to a business that lived or died by its decisions. Running a company teaches lessons that advisory roles cannot. You don’t get to walk away when the client meeting ends. You own the consequences.
At Newmark, Raj served as General Manager and Head of AI & Data. Building AI capabilities inside a large, traditional organization is harder than in a startup. It requires change management, stakeholder alignment, and the ability to convince people who’ve succeeded using older methods that new approaches are worth the disruption. Professional networking resources provide additional background at https://www.crunchbase.com/person/reeve-b-waud.
Why This Background Matters for Reeve Waud
When Reeve Waud appointed Prithvi Raj to lead AI and data efforts at Waud Capital Partners, the firm was betting on a specific kind of executive: someone who understood both the technology and the business, who had built things and advised on things, who had worked inside consultancies and inside product companies and inside organizations managing real transactions. That breadth is rare.
Most private equity firms hire CAIOs with deep AI expertise but shallow understanding of PE. Or they hire operators with strong P&L track records but limited AI sophistication. Raj’s resume suggests someone who could code, who understood technical architectures, but also someone who’d spent years teaching non-technical executives how to think about data-driven decisions. The Princeton engineering degree grounds him in fundamentals. The Harvard MBA signals comfort with strategic thinking. The consulting, tech, and operating roles reveal a person who could translate between technical teams and business leaders-the skill that actually determines whether AI initiatives at portfolio companies create value or simply consume resources. Reeve Waud’s hire reflects a specific conviction: that PE success in the AI era requires leaders who aren’t pure technologists and aren’t pure operators, but rather people who’ve spent enough time in both worlds to navigate them with ease. Prithvi Raj’s path suggests exactly that kind of balanced expertise.
