Eric joins Tala as Chief Technology Officer after leading engineers, data scientists, and AI infrastructure specialists at Grammarly. Before that, he held senior data and engineering leadership roles at Stitch Fix, Yelp, and LinkedIn. He holds a Ph.D. and an MBA from the University of Chicago Booth.
As CTO, Eric will lead Tala’s engineering, data, and AI functions, with a focus on the platform infrastructure that supports Tala’s work across lending, payments and financial services.
Tell us about your background.
I started my career in data science and spent years building toward larger technical organizations with deep product focus. At Yelp and LinkedIn, I built data infrastructure and measurement at scale. At Stitch Fix, the work shifted to embedding machine learning and experimentation as the core operating system. The models drove decisions end-to-end, and that required very different thinking about how you build, validate, and maintain technical systems.
At Grammarly, I led a global team spanning engineering, data science, data platform, and AI infrastructure. Managing that scope taught me a lot about keeping quality high as teams and systems grow and ensuring that growth is tightly connected to product and business velocity.
I also hold a Ph.D., and that educational background and love for teaching shapes how I approach platform problems. Understanding why something works, not just that it works, tends to produce better systems over time. Being able to teach others about that system shows that you understand it at a level where your opinion counts.
What drew you to Tala?
I actually learnt about Tala about a decade ago while living in Southern California, and I thought that the mission was incredible – it resonated with me. Now being a leader at Tala ten years later and seeing the foundational architecture behind the scenes makes me excited to scale it to the next phase to serve more of the global majority.
Tala has built something that takes years to accumulate: proprietary data from customers in markets that traditional financial institutions largely don’t serve, models trained on that data, and infrastructure to act on it. This technical advantage can’t be easily replicated and gives Tala a head start on others who might move into the space.
I’ve spent my career building data and AI systems, and the question of who benefits from those systems is one I think about. Tala’s mission deeply resonated with me because it focuses on customers that are far outside of the tech world. Tala is using its platform to expand credit access for people who have largely been shut out from accessing it.
I’m also joining Tala at a critical time in our industry. Modern payment infrastructure is changing what’s possible for companies operating across borders, and Tala is well positioned to move with that shift. I wanted to be part of building the technical foundation that makes it happen.
How do you think about building and scaling technology platforms?
First, systems need to solve a real problem. I challenge teams to only build platforms once there are three or more clear use cases where unified infrastructure is a competitive advantage. I don’t believe in the “if you build it, they will come” approach. Second, before scaling products on top of infrastructure, the underlying systems need to be reliable, observable, and maintainable. Companies that skip the “prove it” moment and don’t think about what a platform needs to be great at slow down later, often at the worst moments, or build something that has no real demand.
The second piece is the relationship between data and decisions. A strong platform doesn’t just store and retrieve information. It improves over time, surfaces the right signal at the right moment, and makes the people building on top of it more capable and more efficient. Treating data and AI infrastructure as a core asset rather than overhead is something I’ve seen make a real difference in how companies grow. This is particularly important in the AI moment we are in where everyone is searching for what sets them apart.
The third is team structure. Technical systems reflect the organizations that build them, starting with clear ownership that sets high craft standards. Establishing a culture where engineers feel accountable for outcomes matters just as much as any architectural decision. I think about engineering organizations as go-to-market-minded builders. Everyone building has a responsibility to understand how the business generates revenue, to know what its customers want, and to not wait for others to give them priorities.
What trends or technologies are you most focused on for Tala’s platform?
AI systems in production are the first. Many organizations have run AI experiments, but fewer have built the infrastructure needed to run those models reliably at scale, keep them updated, and audit their behavior. For Tala, getting that right matters across credit decisioning, predictive models, and customer intelligence. The success of the business and enablement of the global majority depends on it.
The second is the shift in financial infrastructure. Stablecoins and programmable payment rails are changing what’s possible for cross-border financial services. The possibilities for serving customers didn’t look like this five years ago, and Tala’s platform has embraced this shift to onchain finance and will need to continue operating at the pace of innovation.
The third is proprietary data. As models improve, organizations with richer, more reliable data assets will increasingly outperform those without them. Tala has built that asset over years of operating in markets where others haven’t. Making sure we get full value from it and keep building on it is a core part of what I’m here to do.