Abhishek Dasoar is Director of Advanced Analytics at Airbnb, where he leads data and AI initiatives focused on driving business impact across the platform. His career has evolved from hands-on analytics and engineering into leading large-scale data and AI transformations at global consumer and marketplace companies.
Abhishek began his career building and working closely with data systems, developing a practical understanding of how they function and where they break. Early roles grounded him in technical execution, while later experience at companies such as eBay and Twitter expanded his perspective on how analytics creates value only when it informs decisions across product, operations, and go-to-market strategies.
A defining shift in Abhishek’s career came as he moved from individual contributor roles into leadership, building, and scaling global analytics teams. In doing so, he transitioned from being the expert to enabling others and establishing clear metrics, embedding experimentation, and creating operating rhythms that drive consistent execution.
At Airbnb, Abhishek has worked at the intersection of trust, fraud, and platform integrity, shaping his view of AI as a tool for leverage rather than an end in itself. He emphasizes that AI delivers impact only when tightly integrated with human workflows, aligned incentives, and measurable outcomes.
As a data and AI leader, which traits and skills do you think matter most, and which of those have been most influential for you in your current position?
“Effective data and AI leadership rests on a combination of judgment, influence, and execution. First are business context and decision clarity and the ability to frame the right questions and translate data into choices leaders can act on.
“Second, systems thinking, understanding how models, metrics, incentives, and workflows interact, and where AI will genuinely change outcomes versus add noise.
“Third, influence without authority: aligning product, engineering, operations, and policy around shared goals and trade-offs.
“Finally, operational discipline matters with clear metrics, experimentation, and accountability so insights consistently turn into results.
“In my organization, the most influential of these has been systems thinking paired with influence. The biggest gains came from embedding analytics and AI into everyday decision flows (agent tools, prioritization logic, and operating rhythms) so teams trusted and used them. That’s what allowed data and AI to scale from insight generation to sustained business impact.”
Reflecting on your career, what is one non-traditional piece of advice (outside of technical skills) you would give to an aspiring data or AI leader aiming for the C-suite?
“One non-traditional piece of advice I’d offer is to stop optimizing for being right and start optimizing for being trusted. As you move toward the C-suite, your impact depends less on analytical rigor and more on whether leaders believe in your judgment under ambiguity. That trust is built by understanding incentives, timing, and trade-offs, not just data.
“Some of the most consequential moments in my career weren’t about better models, but about knowing when to push, when to simplify, and when to accept an imperfect decision so the organization could move. Aspiring data and AI leaders who learn to balance conviction with pragmatism are far more likely to earn a seat at the table than those who only optimize precision.”