logo

Harbingers’ Magazine is a weekly online current affairs magazine written and edited by teenagers worldwide.

harbinger | noun

har·​bin·​ger | \ˈhär-bən-jər\

1. one that initiates a major change: a person or thing that originates or helps open up a new activity, method, or technology; pioneer.

2. something that foreshadows a future event : something that gives an anticipatory sign of what is to come.

cookie_image

We and our partners may store and access personal data such as cookies, device identifiers or other similar technologies on your device and process such data to personalise content and ads, provide social media features and analyse our traffic.

introduction image

Picture courtesy of: Henry Baker

Article link copied.

“Japan changed how I see the world”: Henry Baker’s journey from history to data science

author_bio
Katie Chen in Nagasaki, Japan

17-year-old interviewed a historian-turned-data scientist about his unusual career path

Henry Baker didn’t take the typical path into data science. With a history degree from Oxford and six years in Japan working on technical and educational issues related to Sub-Saharan Africa, his story is a testament of curiosity, adaptability and the power of interdisciplinary thinking.

“I’m just interested in how the world works,” Baker, 32, told me in Nagasaki, explaining his shift from studying modern history to researching data science. Now a data science researcher at the Global Public Policy Institute (GPPi) in Berlin, he sees the two fields – though seemingly worlds apart – as parallel approaches to the same question: why is the world structured the way it is?

“History tries to interpret meaning through qualitative analysis, while data science quantifies that same complexity through numbers.”

A surprising connection between the two came to him via science fiction. As a teenager applying to college, he wrote his Oxford personal statement on Isaac Asimov’s Foundation series, particularly fascinated by the concept of “psychohistory” – a fictional discipline combining statistics and sociology to predict the future. Years later, he realised that psychohistory foreshadowed the core aims of modern data science: modeling the world through agent-based simulations.

Before immersing himself in data, however, Baker took a detour – one that led him across the planet to Japan. He moved to Japan for a job focused on East African education access, working with NGOs in Uganda and Senegal. “I didn’t come to Japan for Japan,” he said, “but I ended up staying almost six years.”

Living and working in Japan shaped his views profoundly. “It was my first major job, and I internalised a lot of the Japanese work ethic, sometimes to my own detriment,” he said, citing long hours, rigid hierarchies and a culture of presenteeism.

Despite the challenges, he treasured the experience. “I learned Japanese. That alone was harder than studying at Oxford or learning data science. It completely reshaped how I think about language and meaning.”

Baker emphasised that these experiences, though seemingly unrelated, all contribute to his current research. “Oxford taught me how to learn independently. Japan taught me resilience. Data science lets me ask new questions about the world.”

He also credited his visual thinking style with helping him grasp complex mathematical concepts, particularly linear algebra, the foundation of many machine learning techniques.

“Follow what genuinely interests you”

On the topic of AI, Baker offered a unique view. While many experts predict exponential progress, he suspects we may be reaching a plateau with current large language models.

“There’s only so much intelligence you can extract from language prediction. It might sound human, but that doesn’t mean it thinks like a human.”

Unlike Asimov’s vision of Psychohistory, today’s AI is not yet capable of using data science to predict human society.

Baker is also concerned about AI’s impact on the job market. Unlike the future of AI, its impact on jobs isn’t a future problem, it’s happening now. “Knowledge work, especially entry-level research or coding jobs, is already being automated. That changes everything for people entering the workforce.”

Despite this, he remains hopeful. For young people navigating an unpredictable world, his advice is simple: “Follow what genuinely interests you, even if it doesn’t seem immediately practical. I gave up math at 16 and now I teach it. I learned Japanese for work, and now it’s something that makes me stand out in unexpected ways.”

Henry Baker’s career is a reminder that not all paths are linear. The most valuable skills, he believes, are flexibility, curiosity and the willingness to learn, traits that have taken him from ancient Rome to machine learning, from Tokyo offices to Berlin research labs.

“Everything I’ve done felt like starting from scratch,” he concluded, “but every step has made the next one possible.”

Written by:

author_bio

Katie Chen

Contributor

Shanghai, China

Born in 2007 in Shanghai, Katie studies in Massachusetts, United States. She is interested in math and art and plans to study data science and economics. For Harbingers’ Magazine, she writes about science, human rights, and culture.

In her free time, Katie enjoys playing squash, reading, and art.

Katie speaks English and Mandarin.

2025 japan newsroom

🌍 Join the World's Youngest Newsroom—Create a Free Account

Sign up to save your favourite articles, get personalised recommendations, and stay informed about stories that Gen Z worldwide actually care about. Plus, subscribe to our newsletter for the latest stories delivered straight to your inbox. 📲

Login/Register