TikTok Data Scientist Explains Why AI Can't Replace Human Insight

Data science, an interdisciplinary field combining statistics, computer science, and industry expertise, is all about extracting actionable insights from massive datasets to drive better business decisions and fuel innovation.

In today's fast-paced digital era, data has become the "strategic oil" of the modern economy, and data science has emerged as a key driver of business transformation. Social media platforms, as major hubs of data generation, are among the most active arenas for applying these skills. Take TikTok Application for example: with nearly 1.6 billion monthly active users worldwide, over 150 million in the U.S. alone, and annual revenue surpassing $23 billion, its success is heavily powered by AI and data science. This has naturally sparked debates around whether AI could one day replace data scientists.

Senior Data Scientist at TikTok Inc., CHEN Ruihao, offers a clear answer: "AI tools are fundamentally changing how data scientists work, but we are still far from being replaceable."

With nearly a decade of experience in data science and a background at several leading tech companies, Mr. CHEN specializes in handling petabyte-scale datasets and leveraging advanced machine learning techniques to turn complex business challenges into quantifiable models. At TikTok, he has led projects that not only guide strategic decisions and business growth but also help safeguard platform sustainability and maintain user trust.

According to Mr. CHEN, AI cannot replace data scientists for two main reasons. First, at the business and strategic level, AI lacks the ability to question assumptions or define the right problems-it cannot independently determine what truly drives growth. Second, in terms of risk and compliance, privacy protection and legal responsibility often require cross-department coordination and value trade-offs, tasks that are beyond AI's capabilities.

On the strategic side, a data scientist's value goes far beyond technical execution; it lies in accurately framing problems and making informed, high-level judgments. "Before analyzing data, the first step is always to define the problem we are really trying to solve and determine whether it genuinely drives business growth. AI can execute predefined tasks, but it cannot question assumptions or decide on the right problem to tackle," Mr. CHEN explains.

Consider an e-commerce recommendation system: AI algorithms can optimize immediate conversion rates based on historical data, but they struggle to assess whether these optimizations align with long-term strategic goals or avoid biases that could harm user experience. Business decisions often involve balancing multiple objectives, which requires not only technical skill but also a deep understanding of user behavior, market dynamics, and platform ecosystems. "Models don't operate in isolation," Mr. CHEN emphasizes. "Our responsibility is to identify causal directions that genuinely drive sustainable business growth-a level of complex judgment AI cannot independently handle."

On the compliance side, privacy regulations require interpreting constantly evolving laws and tailoring practical data protection measures to specific business scenarios. Achieving this often involves balancing user experience, commercial objectives, and legal requirements, as well as coordinating across departments. "AI lacks the flexibility to interpret laws, the awareness of responsibility, and the capacity to bear legal risk. It cannot independently manage these highly complex and high-stakes compliance tasks," Mr. CHEN adds.

In the age of AI, the smart approach is not to fear technology replacing humans, but to use AI tools to enhance data scientists' analytical capabilities, enabling them to make more efficient, impactful decisions in complex business and strategic contexts. That's why data scientists remain among the most sought-after and highly paid professionals globally, standing out as some of the hottest talent in the digital era.

(Written by Jessie Epstein)

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