The Data Scientist Who Sharpened Snowflake’s Multi-Billion-Dollar Forecasts Now Builds AI Models That Outpace Claude

- Sashindra Suresh

Artificial intelligence has flooded the market with powerful models, yet reliability remains one of its biggest unsolved challenges. Even the most advanced general-purpose systems continue to struggle with domain-specific accuracy, latency, and data privacy requirements—limitations that often prevent companies from deploying AI in high-stakes production environments.

Engineers and entrepreneurs are now working to close that gap by building systems designed for capability and real-world reliability. Among them is Yuta Baba, a data scientist turned technical founder who is developing specialized AI models aimed at outperforming general-purpose systems in speed, precision, and enterprise deployment: “One of our customers was using an OpenAI model and liked how smart it was, but the speed just wasn’t good enough for their use case. So we built a model that delivered similar performance but ran much faster. That’s the kind of problem we’re trying to solve,” he shares.

That focus on reliability and real-world performance comes from years of hands-on experience. Before launching his startup, he spent nearly five years at Snowflake, where he was one of the first data scientists for one of the fastest-growing data companies in the world.

While working there, Baba developed machine-learning systems that improved the company’s forecasting. His models improved quarterly bookings forecast accuracy by 58.7 percent, helping leadership monitor revenue as Snowflake expanded from roughly $250 million to $3.6 billion in annual revenue.

Building on this work in forecasting, this sought-after data scientist also developed a 10-year financial planning application that he personally presented to the company’s executive leadership team. The system turned complicated forecasting models into simple, interactive dashboards that finance, sales, and operations teams could use to make real-time decisions: “Our goal was not just to simply make predictions. We also wanted teams to understand the story behind the numbers—how things like conversion rates, deal cycles, or pricing changes could shape the company over the next decade,” he explains.

Delivering that level of clarity took more than a simple forecasting model. Behind the dashboards was a sophisticated data infrastructure that processed massive volumes of operational data in real time. Baba built pipelines that integrated data into a unified analytics framework used across the company: “The systems processed more than one terabyte of revenue, bookings, cost, and pricing data through SQL and Python pipelines that became part of the organization’s core infrastructure,” he says.

Those pipelines—developed using large-scale data engineering workflows and analytics platforms such as DBT, Tableau, and Looker helped leaders link day-to-day performance with long-term planning.

Working inside Snowflake during one of the fastest growth phases in enterprise software also reshaped Baba’s understanding of what technology systems could achieve at scale: “You could see how one well-built system could influence decisions across thousands of employees,” he shares.

That realization unfolded as the company grew at a remarkable pace. When Baba joined Snowflake, it had around 1,000 employees and $250 million in revenue. By the time he left, it had grown into a global company with over 8,000 employees and more than $3.6 billion in revenue: “Seeing one product and one team grow so fast completely changed how I see the world,” Baba recalls.

It was in this high-growth environment that this sought-after expert’s talents became impossible to overlook. Baba’s colleagues quickly recognized not only his technical skills but also his approach to challenges with a builder’s mindset. Matt Franking, Baba’s former manager and Director of Data Science at Snowflake, worked closely with him for several years—first as a peer and later as his supervisor. Franking recalls Baba as having that technical depth with a drive to move projects forward: “Yuta approaches problems like a true builder. On top of analyzing systems, he identifies opportunities, proposes solutions, and takes full ownership of execution.”

According to Franking, Baba’s strength came from combining technical data science with practical product thinking. This allowed him to thrive in complex, fast-moving environments where many problems were still unclear or evolving.

Franking also commends Baba’s collaborative leadership and work ethic as qualities that helped the team: “He’s incredibly generous with his time and always willing to help teammates succeed. Whether he’s mentoring a new hire, helping someone untangle a tricky problem, or just getting everyone on the same page to solve a tough challenge, Yuta makes the team click.” That balance of humility, resilience, and initiative, Franking says, set Baba apart from many engineers.

These qualities also didn’t go unnoticed by others who worked closely with him. Andrew Seitz, a former colleague who collaborated with Baba for six years at Snowflake, commends Baba’s combination of intellect and empathy: “I hired Yuta straight out of college as one of Snowflake’s first data scientists, and in every project he tackled, he showed an exceptional talent to organize complex systems. He treated every task—big or small—with the same level of care and organization,”  Andrew shares.

Andrew adds that even during tough personal times, Baba remained cheerful and supportive of his teammates. That resilience and generosity carried over into his professional work. He also lauds Baba’s technical excellence in machine learning and AI applications: “Yuta also designed predictive models that directly influenced millions of dollars in revenue and cost savings,” Andrew adds.

Reflecting on that time, Baba says being part of a rapidly growing company taught him a powerful lesson about building infrastructure: “I realized that if you design the core systems properly, they can grow far beyond what you first expect.”

That experience ultimately planted the seed for entrepreneurship: “It showed me that if the technical foundation is right, a small team can build something that changes entire industries. At some point, I started asking myself what it would look like to build something from scratch. Snowflake showed me what great infrastructure looks like at scale,” he shares.

After leaving Snowflake, Baba co-founded Carrot Labs. He carried the same engineering discipline but on a larger scope. There, he built specialized AI infrastructure that is reliable even under high demands: “Large language models are incredibly powerful, but raw capability alone isn’t enough when running production systems,” he shares.

Christopher Acker, CEO and co-founder of Carrot Labs AI, Inc., and Baba’s longtime business partner, has observed this approach up close: “Working with Yuta, you quickly realize he isn’t just building technology—he’s designing solutions around people and real-world problems. He has this rare ability to translate abstract technical ideas into practical systems that actually solve users’ pain points. Most engineers stop at code, but Yuta always asks: ‘How will this impact the person on the other end? How will it change their day-to-day experience?’ That mindset, combined with his relentless curiosity, is what allows him to consistently deliver AI products that work in the real world,” Acker says.

At Carrot Labs, Baba and his team develop fine-tuned prompt-injection detection models optimized for enterprise workloads. These systems run up to ten times faster than general-purpose models like Claude while reducing false positives by roughly 50 percent—making them significantly more practical for real-time production environments.

This focus on performance and reliability is part of this data scientist’s broader view of where the AI industry is heading: “General models from companies like OpenAI or Anthropic are excellent at about 80 percent of use cases. But many enterprises need 95 to 100 percent accuracy on domain-specific tasks where hallucinations or latency can break the product experience. That’s why specialized systems are needed,” he explains.

To achieve that level of precision, Carrot Labs trains customized architectures tailored to each client’s dataset and operational constraints, rather than relying on off-the-shelf large language models. The system is trained using a reinforcement-learning-style process. Outputs that meet strict performance targets are rewarded, while errors like hallucinations, false positives, or excessive token usage are penalized: “It’s a lot like how people learn. You reward what works and point out what doesn’t. Over time, the model learns how the customer’s environment operates,” Baba explains.

The results are most visible once the system is deployed in a real product. In one case, Carrot Labs worked with an ed-tech client that generates flashcards from uploaded YouTube videos. After implementing the optimized model, end-to-end latency dropped by about 70 percent compared with baseline Claude performance—while maintaining, and in some cases even improving, output quality: “When users had to wait several seconds for results, the product just felt too slow. But after we cut latency by about 70 percent, it felt almost instantaneous. And when something feels instant, people interact with it differently. They stop thinking about the delay and start using the system more naturally,” he explains.

But performance is only part of the equation for companies deploying AI in production. Enterprise clients also need strong guarantees around security and data protection.

Just as important for enterprise clients, the systems can run on private infrastructure or secure endpoints—ensuring that proprietary training data never leaves the customer’s environment: “Data privacy is a major concern for many organizations. Running models in controlled environments gives companies confidence that sensitive information stays protected,” Baba shares.

Building systems that address both performance and enterprise constraints has also shaped Baba's approach to leadership at Carrot Labs.

Beyond the technology itself, this highly-regarded data scientist represents a new breed of technical founder who owns both the engineering stack and the business strategy behind it. At Carrot Labs, he handles model development while also leading customer discovery, cold outreach, pricing strategy, and internal tooling: “I still write every LinkedIn message myself. No automation. Personalization matters when you’re trying to understand a customer’s real problem,” he shares.

He also built the company’s website and a real-time token-usage monitoring dashboard that allows teams to track model consumption and system performance across deployments.

That hands-on approach extends across the entire technical stack. Baba works directly with the tools that power the company’s infrastructure, allowing him to move quickly from experimentation to deployment.

This full-stack ownership reflects a technical toolkit spanning Python, R, SQL, and React, as well as machine learning frameworks such as NumPy, pandas, and scikit-learn.  With them, he builds end-to-end pipelines that connect machine-learning models directly to production systems: “I iterate faster today than ever before in my career. With modern AI tools, I can ship production code, dashboards, and features in days instead of weeks,” he shares.

That passion toward rapid experimentation was shaped by several earlier entrepreneurial experiments.

Before founding Carrot Labs, Baba briefly launched CareVo, a two-sided senior-care marketplace he built end-to-end in Supabase and React. At CareVo, Baba designed the entire system architecture himself—including matching algorithms, supplier onboarding systems, and an insurance eligibility engine that translates Japan’s complex long-term care policy rules into automated service recommendations.

Through  extensive customer interviews and product iterations, the platform onboarded 30 care service suppliers, giving Baba early experience in building products directly informed by user needs.

This achievement carries significant weight for the broader field of technical entrepreneurship. Technically, it showcases Baba’s rare full-stack capability: he single-handedly designed and implemented the entire marketplace architecture—including complex data models, intelligent matching logic, operational workflows, and a sophisticated care-insurance simulation engine that translated intricate regulatory policy rules into precise, executable decision logic.

The experience also helped shape his approach to venture building. In 2025, he was selected for Antler Japan’s Founder-in-Residence program, where he refined product-market fit and venture-scale go-to-market strategies before ultimately launching Carrot Labs.

Long before these entrepreneurial ventures, however, Baba’s interest in data and quantitative analysis had already taken root. His journey into data science began at Carleton College, where he earned a Bachelor of Arts with distinction in Statistics and History. He also received the Grew Bancroft Foundation Scholarship for his excellence in quantitative research: “Going into tech was not my initial plan. I wanted discussion-based humanities classes because the Japanese and American education systems felt so different. But I started taking statistics courses, and eventually fell in love with data,” Baba shares.

Even during his undergraduate years, Baba’s research hinted at the direction his career would take. As a co-author on a paper presented at the NeurIPS 2020 Workshop on Machine Learning for the Developing World, he helped design scalable algorithms for matching translators with displaced persons facing language barriers—an early example of applying machine learning to real-world coordination problems: “The project showed me how algorithms could help solve coordination problems at scale. That idea has stayed with me ever since,” Baba says.

Today, that same philosophy guides his work at Carrot Labs. Rather than starting with the technology itself, Baba begins with the practical coordination problems companies face when deploying AI systems.

Everything begins with a customer problem: “We step in when customers need accuracy, speed, and reliability because these actually determine whether an AI product works in production,” he explains.

This focus on production-ready performance reflects a broader shift taking place across the AI industry.  If AI is to fulfill its promise, it will be engineers like Baba—quietly solving the hardest infrastructure problems—who make that future possible.

The same engineer who once built forecasting systems guiding billion-dollar decisions at Snowflake is now designing faster, more accurate AI models that companies can reliably deploy in production. As artificial intelligence evolves beyond monolithic foundation models toward specialized systems, founders like Baba shape the next generation of infrastructure: “AI is moving so fast it feels like science fiction. Every day I wake up excited to see what’s newly possible,” he shares.

About the Author

Sashindra Suresh is an experienced writer specializing in artificial intelligence, software development, and emerging technologies. With a strong ability to translate complex technical concepts into clear, engaging insights, she has contributed to a wide range of publications and platforms. Her work focuses on making cutting-edge innovations accessible to both industry professionals and curious readers alike.