Where are the Vietnamese data science candidates?

Students often apply to numerous internship applications throughout the semester, aiming to secure positions that might result in full-time employment after the internship concludes. As someone who has experienced the internship hiring process from both perspectives, I have noticed that I have never interviewed any Vietnamese candidate during my entire tenure at my current company.

First, internship candidates can be surprisingly more qualified than some of the full-time candidates we have interviewed and extended offers to. Faced with such high qualified internship candidates, our team must carefully evaluate whether they would make suitable full-time offers down the road. A potential factor contributing to this trend is that companies intentionally target top-performing programs to source talents.

Despite the efforts of sourcing talents from diverse backgrounds, and my personal outreach to Vietnamese professional and student networks, I noticed a striking absence of Vietnamese candidates reaching the final interview stage. My heart raced when I spot a Vietnamese name among the shortlisted candidates. Over my career, among all Asian candidates, I have primarily encountered Indian and Chinese international students in interviews. As a Vietnamese professional working in data science, I could not help but ask the question: why don't Vietnamese students seek internships with my company compared to other nationalities? Could it be due to perceptions about the prestige or appeal of my company, an old life insurance company versus large tech companies like Google, Meta or Microsoft? Yet, many students from China and India still pursue opportunities with us despite those perceived differences. Our interns last year all hailed from India. They were some of the most talented data scientists that I have had a chance to work with.

According to the Open Doors report by the International Institute of Education, Vietnam ranks number five in the place of origins in sending students to the United States. In the academic year of 2022/2023, almost 22,000 Vietnamese students are studying in the United States, while China and India send close to 290,000 and 270,000 students respectively. This translates to an approximate proportion of 13 Chinese or Indian students to one Vietnamese student. However, in the professional field of data science, it does not seem to be my observation. Every year, our roster of short-listed candidates does not even have one Vietnamese student that makes it into the final interview round out of 50 students being selected for the interview rounds. So, the numbers are not enough to explain the reasons for the absence of Vietnamese candidates.

I checked this observation with another Vietnamese data scientist at Q., a large tech company, who confirmed my observation that Vietnamese candidates are a rarity in our field despite the global data science boom since the early 2010s. Thomas Davenport and DJ Patil wrote for the Harvard Business Review and called it "The Sexiest Job of the 21st Century."

What is it about the Vietnamese higher education, or the way that Vietnamese students in Vietnam and in the U.S. study or choose their career paths that explains their absence in professional data science now?

My observation is also shared by researchers in social science who examine the issue of diversity in high-paying jobs in tech. When I was in graduate school, I had a chance to attend a book talk by sociologist France Winddance Twine. The book, titled "Geek Girls", examines how women of color such as Asian women, Black and Latino women end up working in high-paying jobs in Silicon Valley. Her research found that most female engineers especially Indian female engineers possess something called "geek capital," a form of cultural capital, refers to the ability to relate to the knowledge, abilities, and cultural competencies within the realm of geek cultures or STEM fields. Most of the times, it means they are embedded in a social network that is directly plugged into the tech culture. Some Indian female engineers stated that the reason that they became engineers or took engineering degrees in college were because they had parents or siblings who were engineers, which made it easier for them to become engineers. After the talk, I raised the question why Asian women in Silicon Valley do not help Black and Latino women in Silicon Valley to get these cushion engineering jobs that could pay up to $300,000 a year. Hearing my name, the speaker immediately made an educated guess that I am Vietnamese. Then instead of answering my question, she stated a painful fact: Vietnamese are underrepresented in Silicon Valley unlike Chinese and Indian.

The question of why Vietnamese workers are underrepresented in the high-paying tech work place in comparison to Chinese and Indian counterparts has bothered me for the past few years. There are many folk theories floating on social media to explain this phenomenon. Some posit that Indian and Chinese students help each other during internship and job interviews. For example, they would create very tight knit groups to prepare for exams akin to a college entrance exam preparation. Since the Chinese and Indian engineering education is so competitive, cramming for exams has become a regular practice, and they repeat the same exercise in the context of the United States. This explanation is necessary but not sufficient. Something goes deeper than just this group solidarity explanation. When I decided to leave the life of a social science researcher (I was trained as a sociologist with a PhD in sociology), I also prepared for similar technical interviews. I also got help from a lot of Vietnamese fellows in the United States who were working in tech and finance. However, I was the only person who prepared to interview for a data science position. I mainly figured out things on my own relying on other folks’ suggestions of what to do. It was a lonely experience in many ways. I wish I had a study group that enabled me to prepare for the interviews more effectively.

To better understand this observation/phenomenon of the relatively fewer Vietnamese machine learning, data science candidates, examining the educational policies that have been put in place in China, India, and Vietnam for the past five decades can shed light on additional reasons. India, as a nation, decided early in the 60s that they wanted to be in the information technology (IT) business, and built an entire educational infrastructure called the Indian Institutes of Technology (IITs), the top engineering universities in India. These schools were built with help of top private engineering schools in the United States such as Stanford, Cornell, MIT. IITs modeled their American counterparts in putting campuses in remote areas to have a more isolated campuses for students to immerse in a well-rounded engineering education. IITs with the international connection created a pipeline to send students to study abroad, earning PhDs in engineering fields in the United States. China has pursued this slightly differently by creating different tier systems where tier-1 schools such as Tsinghua, Peiking, Fudan, Shanghai-Jiao Tong universities also provide rigorous engineering education. Graduates from those schools pursue PhD in the United States after their college education, and their school names are often recognized by admission committees. Both countries pursue a top-down approach with engineering education such that engineering education is both rigorous, subsidized, and could produce out many outstanding candidates that have very good fundamental math education. Recently the Vietnamese government approved a National Digital Transformation Program, aiming to become one of the top AI players in the ASEAN region by 2025. Engineering education and AI education have received more support to enable this national ambition. As a result, it might take a few years for the labor market both within Vietnam, and globally to really observe the effects of such a national investment in tech talents.

Moving from a structural explanation to a personal one, I come back to another reason why I felt so lonely and doubtful during the job application time. I reflected at the question why I did not get an undergraduate education in engineering or computer science. I think it was a mistake. As a high school student, I earned a bronze medal in Vietnamese national Informatics Olympiad. One would think that I’d make a decent engineer. I had solid mathematical reasoning, an ability to follow algorithmic reasoning, and could code, and learn how to code in a new programming language relatively quickly. The culprits of my not becoming an engineer during my 20s were my parents, or more extensively my extended family network. They somehow convinced me when I was 17 that I would not be happy being one of the only five female students in 100 students at computer science undergraduate major at Hanoi University of Science and Technology. Instead, they thought I would be happier studying at Foreign Trade University. They were half right. I was very bored at Foreign Trade University with the academic life there, where lessons didn’t feel challenging enough. So, I decided to call it a quit, and found opportunities to study abroad instead. I ended up studying at a small liberal arts college in the United States, which allowed me to explore everything except engineering. Yet I still wanted to do some math, so I majored in mathematics-economics. Life went on until I was in my last two years of my PhD in sociology when I had to decide whether I wanted to become a professor of sociology, or deciding to choose a different career that requires upskilling and exploring a new territory. I ended up choosing data science because it is more like a research career than any other career in tech such as software engineering, which I am anyway not equipped to do straight out of graduate school.

Reflecting on my long-winded path that led me to my current profession, I realized that unlike other women in Silicon Valley that Sociologist Twine interviewed, I had no role model of a successful engineer in my immediate family, or friend circles who would show me how to become a successful female engineer in any field. My extended family members were all working in banking; thus, I had no mental image of what it meant to become a female engineer in a male dominated field like software engineering. Sometimes I asked if my path would have been smoother if I didn’t buy into my parents’ arguments about how difficult my social life would have been if I decided to do an undergraduate degree in engineering.

What I have learned from this journey is that there is a confluence of factors that there are very few Vietnamese candidates whose resume ever ended up on my desk. Some of it has to do with the lack of role models who are successful engineer, and the (female) candidates themselves do not choose Data Science, computer science because it’s a male dominated field, and that it’s a relatively new field that do not dominate the popular imagination as in whether it’s a good career. Another and more significant reason I would argue is about the structure of the Vietnamese higher educational education that doesn’t yet prioritize AI development, data science, and practical engineering. This is currently changing, but I hope that changes come faster, and that more Vietnamese students will choose data science as their career paths and apply to data science internship and job opportunities.

*Nga Than is a senior data scientist, living in New York City.