From Global Business to Scientific Discovery
Dmitrii Timoshenko’s journey from managing national markets at Yandex to driving innovation at Amazon is a compelling story of reinvention, intellect, and vision. With a career that bridges commercial leadership and scientific rigor, Timoshenko exemplifies what it means to be a modern tech applied scientist—someone who not only builds machine learning systems but also understands the strategic levers of business.
Today, as a researcher in Amazon’s Santa Clara office, Dmitrii works on the cutting edge of artificial intelligence, applying his expertise to solve complex problems in quantitative marketing. But his path to Silicon Valley success was anything but conventional.
Timoshenko’s early career was forged in high-stakes business environments. He pioneered the international expansion of Yandex—Russia’s largest tech company and a rare global competitor to Google—into emerging African markets such as Zambia, Ghana, and Angola. “In that role, you learn how to build a business from scratch,” he recalls. “You manage budgets and partnerships—but more importantly, you learn how to ask the right questions.”
These questions weren’t just managerial—they were deeply quantitative. How does pricing affect user retention? What signals indicate customer intent? How can advertising systems be optimized in real time? Timoshenko realized that to answer these questions at scale, he needed more than business instinct—he needed science.
Bridging Berkeley and Big Tech
His transition from business and operations leader to researcher was deliberate. Timoshenko returned to academia, diving deeper into statistics and immersing himself in machine learning at UC Berkeley as a graduate student. He quickly distinguished himself as a rare hybrid: someone fluent in both business strategy and statistical modeling.
That dual fluency led him to Amazon, where he joined as an applied scientist working on marketing measurement systems. His focus? Quantifying the impact of advertising using large-scale causal inference methods—an area where traditional marketing meets high-dimensional data science.
“In big tech, marketing isn’t only about storytelling. It’s about algorithms, models, and provable ROI,” Timoshenko says. “That’s where I come in.”
His work combines methods from econometrics, statistics, and machine learning to evaluate how different channels—from video ads to sponsored search—contribute to revenue. He’s built systems that automate this analysis across thousands of campaigns, enabling Amazon to optimize spending and maximize return on investment.
Rethinking Attribution in the Age of AI
Dmitrii’s approach represents a shift in how marketing is understood within tech giants. Instead of relying on heuristics or vanity metrics, his team builds tools that estimate the true incremental effect of every dollar spent.
One of his recent projects involved developing a scalable attribution framework—technology that identifies which channels matter most in the customer journey by using causal estimates for greater accuracy. It’s a subtle but powerful shift, one that fundamentally changes how campaigns are evaluated.
“Without causal inference, you might think your ad worked—when in fact, those users would have converted anyway,” he explains.
Such insights are reshaping performance marketing, allowing teams to reallocate budgets based on real impact. And the implications go beyond Amazon. As more companies adopt AI-driven marketing platforms, Timoshenko’s work serves as a blueprint for how to measure and optimize advertising in a world saturated with data.
A Rare Hybrid: Operator, Researcher, Visionary
What makes Timoshenko’s story unique isn’t just the technical depth of his current work. It’s the rare combination of hands-on business experience and scientific curiosity. At Yandex, he was negotiating with governments and launching products across borders. At Amazon, he’s writing papers and building systems used by global teams. That range gives him an edge when collaborating across functions—whether he’s explaining a Bayesian model to engineers or framing an A/B test for marketers.
Looking ahead, Dmitrii is excited about the intersection of generative AI and marketing analytics. He’s exploring ways to combine large language models with structured data to build smarter decision systems for advertisers.
For those seeking to bridge the worlds of business and science, his path offers both inspiration and a challenge: think deeply, act boldly, and never stop learning.