Introduction

The tech industry is currently experiencing a significant transformation, driven by advancements in Large Language Models (LLMs) and Artificial Intelligence (AI). In this rapidly evolving landscape, the contributions of data scientists are crucial as tech companies navigate increasingly complex user behaviors and a fast-changing global environment. Data scientists like Yorgos Askalidis from Spotify provide essential insights that help these organizations adapt and thrive. By seeing these developments from the inside, Yorgos plays a key role in translating complex data into strategic decisions that improve user experiences and drive team performance at companies he has worked at, such as Spotify, Meta, and eBay. His efforts not only boost operational efficiency but also lay the groundwork for continuous innovation, ensuring that these tech firms not only keep pace with changes but also lead them.

From Athens to Silicon Valley: A Journey Fueled by Curiosity and Determination

Yorgos began his academic path in Athens, studying mathematics, before moving to a Ph.D. program at Northwestern University in the United States. This shift from a theoretical focus to an environment where problems remained mathematical but were driven by practical applications was key in shaping his career.

"Take one step at a time. That’s my life motto and pattern of my story."

"The general environment in the department of mathematics at the University of Athens fostered an ethos where studying mathematics was considered the goal itself, practical applications not being a priority—perhaps even considered almost taboo. Whereas in the U.S. education generally leans into the real-world applications and how to best prepare you for the job market."

His first step into the professional world was an internship at eBay, which underscored the importance of applying academic knowledge in industry settings. Reflecting on the process of securing this position, Yorgos explained it as a "numbers game," emphasizing the strategy behind his many applications and interviews.

"It’s about increasing your odds through persistent effort and not getting discouraged by initial rejections. Often, the hardest part of getting into the job market is that first job, and once you get your start, you can have the opportunity to gain hands-on experience and have demonstrable impact when looking for new jobs down the line.”

As he adapted to the demands of Silicon Valley, how did he apply these learned principles to adjust to the shifting trends of technological change?

Being a Good Partner in Data-Driven Decision Making

In a team setting, the data scientist uniquely holds the key to detailed data analysis, positioning them as essential in guiding the strategic direction based on that data.

This specialized insight enables them to act not just as analysts but as crucial advisors who translate complex datasets into actionable recommendations for the team and stakeholders.

Yorgos highlights the importance of data scientists in navigating decision-making challenges, particularly when faced with incomplete data. He emphasizes the concept of the "non-null" response.

"When stakeholders ask questions that the existing data can't fully answer, it's not sufficient to simply say we lack the data. Instead, as effective data science partners, we must strive to provide the best possible estimates, openly stating any shortcomings and assumptions. This ensures that stakeholders are always presented with a path forward, however tentative it may be."

In support of this approach, he employs a straightforward yet effective approach called the traffic-light framework to guide leadership in making informed decisions. This framework classifies decisions and tasks into three categories: green for recommended next steps, yellow for feasible but potentially suboptimal choices, and red for options that are possible but not advised according to his analysis. This method helps leaders visualize their options and trade-offs, enabling them to make well-informed decisions. This framework for decision-making has proven effective, but how does it translate into actual project execution?

From Concepts to Execution in Real-World

Throughout his career, Yorgos utilizes the art of storytelling to transform complex data sets into compelling narratives that drive strategic decisions across the organization.

"Data science is not just about numbers; it's about conducting in-depth technical analysis and then simplifying that complexity into a clear narrative, allowing leaders to focus on what matters most to them: making informed decisions.”

This approach to data helps ensure that insights are not only understandable but also actionable. He also discusses how data scientists can provide value beyond technical analysis by driving complex projects forward through coordination, setting timelines, and ensuring smooth execution, ultimately enhancing both the scope and impact of the projects. Through such integration, teams can operate more cohesively and respond dynamically to changing business needs.

More than 8 years into his data science career, Yorgos’s focus is now also shifting toward mentoring and guiding the next generation of data scientists. Maybe even explore more involvement with the local academic institutions as well. Nashville is home to some of the nation’s top universities, including Vanderbilt University and Belmont University, which consistently produce a strong pipeline of highly skilled data science graduates.

Trends to Watch: LLMs, Evolving Roles & Team Dynamics

When asked about the most impactful trends in data science today—particularly within tech product teams—Yorgos pointed to several shifts that are changing how data scientists work and where their value lies.

One of the biggest themes is the widespread presence of Large Language Models (LLMs) like ChatGPT and Copilot in the workplace. He shared how these tools are being actively explored by individuals trying to understand how they can help them do their jobs better.

"LLMs are always in the mind of people recently—how to use them to make themselves better at their jobs, what these tools mean for the job."

"They are trying to understand how something that used to take them 5 hours or even days now takes a few minutes—and then, okay, what does this mean for me?"

This shift raises broader questions—will teams hire fewer people, or will individuals simply do more with the time they save?

At the same time, answering specific questions is getting easier thanks to better tools and systems. As a result, the real value now comes from strategic thinking and offering broader perspectives beyond data.

"Everything gets easier… so the value you bring is less."

"A data scientist can also be a thought partner—offer opinions, go beyond what’s in the data."

Finally, the talent pool has grown. Bootcamps are less in demand, skills are more common, and tools like Copilot mean it’s not just about writing code—it’s about what you bring.

Guidance for Aspiring Data Scientists

Yorgos advises aspiring insights professionals to:

"Always focus on driving decisions, simplifying complex analyses as much as possible for stakeholders, while ensuring the stakeholder remains fully informed and the data scientist’s process is transparent and trustworthy."

In his approach to understanding user behavior, the reasons behind it, and their needs, Yorgos emphasizes the importance of collaboration with user researchers who focus on qualitative insights. By bringing small groups into labs for in-depth feedback, data scientists can uncover meaningful user behavior that often goes unnoticed in larger datasets. This qualitative data, he believes, can illuminate patterns and inform product development in ways that numbers alone cannot.

He also underscores the importance of staying up-to-date with industry trends through blogs, conferences, and thought leaders. Being well-informed allows data scientists to remain at the forefront of emerging technologies and best practices.

“LLMs are revolutionary in many aspects of AI and machine learning. There’s no doubt they’ll shape the future—but I truly believe humans will remain crucial in guiding and using these models. We need a balanced approach, where expertise and intuition still play a key role in making sure LLMs are applied effectively and ethically in real-world scenarios.”

Yorgos Askalidis's career is driven by a passion for the tech industry, user experience, and a positive, can-do attitude. His collaborative spirit and curiosity enable him to learn from those he works with, grasp the priorities and needs of his organizations and leaders, and deliver actionable insights that keep the user experience at the forefront. By being a true partner to his leaders and stakeholders, he empowers them to make better-informed decisions.