When Alexandru says his work matters, he means it. In the two years since he joined Publicis Sapient as part of an acquisition, he’s been embedded with a single client’s central marketing department, helping them turn big, noisy promotional plans into measurable business outcomes. His role sits at the intersection of marketing strategy and machine learning: building models and tools that show where investment should go, how campaigns are actually performing, and how those investments will pay off over time.
In his current work, Alexandru has built an investment distribution model which in simple terms, is a custom system designed to recommend how marketing budget should be allocated across a client’s channels and regions to maximize the return on investment. “We’ve developed a secondary custom model designed to analyze campaign performance causally, not just correlationally, meaning we can show if the campaign actually caused an uplift in performance or not. We also can use it for forecasting to help stakeholders set expectations and plan for future investments.” Alexandru has also had the opportunity to build tailored marketing “agents”. These are automated systems that assist with campaign optimization, audience targeting, and experimentation. Given Alexandru’s journey to Publicis Sapient was through an acquisition, I was curious to understand what made AI at Publicis Sapient exciting to him. He pauses and highlights two things. “It’s the variety and the culture. At PS, teams regularly share demos and presentations, so everybody stays current with the ever-changing landscape. That means engineers get visibility into many different use cases and implementation styles. This doesn’t always happen in single-client or siloed roles. The environment is collaborative rather than competitive with people working toward common goals and actively helping one another learn.”
Like the rest of us, Alexandru is amazed at the pace of change in the world of AI. “One of the things that has struck me is the pace of model development has accelerated dramatically. What might have taken a week to prototype can now be done in a day with modern AI tools, provided you know what to ask, what to look for, and what results to expect. That speed is powerful but it requires care,” he says. “Not all shortcuts are safe or valid, and critical thinking is still essential when working with these new tools.” Critical thinking is just one of the many skills Alexandru points to to be successful in the world of AI and machine learning. “Sure, there are technical skills like Python, databases, and common machine learning libraries that are important, but those can all be taught,” he says. “I believe the qualities that make someone thrive at Publicis Sapient goes beyond code. It’s the curiosity and eagerness to learn. The ability to be open and willing to share and receive knowledge combined with a team-first mindset. This is what I believe will lead someone to success not just at Publicis Sapient but in the world of AI and machine learning as a whole.”
