6 Questions with an IBM Data Scientist and AI Engineer

Sina Nazeri’s list of achievements is as inspiring as it is challenging to package into a simple introduction. His current title at IBM is senior data scientist and AI engineer, but he’s also an instructor, technical writer, and author of several courses. Nazeri’s professional experience spans seven years and is fortified by a decade of academic endeavors, including a bachelor of engineering, a master of intelligent systems, and a PhD in interactive arts and technology. He also holds a variety of skills-based credentials from online learning platforms like Coursera. Over the next six questions, you’ll learn how Nazeri shaped his career in the data science industry, and how you can nurture your own appetite for growth.  

Skill evolution in the modern workplace

Studies suggest that 44 percent of workers’ core skills will change by 2027 due to technology, with analytical thinking, systems thinking, and AI and big data among the top 10 skills on the rise [1]. The rapid pace of technological advancements and competitive nature of today’s job market are placing increasing emphasis on continued learning. Interestingly, the World Economic Forum placed curiosity and lifelong learning at number five in the list of top 10 skills in 2023. 

I am a strong advocate for self-learning. 

“I am a strong advocate for self-learning,” Nazeri says. “While I pursued my master’s and PhD, I found that my research required a deep understanding of data science and machine learning, which was scarce in formal academic courses. This led me to seek out different channels to learn. Online resources and open communities, often mostly free, are your best friend for learning and achieving success.”

Which skills are you surprised that you utilize most often in your role?

In this industry, problem-solving skills are most valued. They enable me to address complex challenges and develop innovative solutions. Meanwhile, in academia, curiosity is more important. This curiosity drives me to delve deeper into data, ask probing questions, and explore unconventional approaches. However, self-learning is most important in both. It allows you to continuously update your knowledge and adapt to new technologies and methodologies to remain effective and relevant in the field.

What tools have you had to master to be successful in your career?

Statistical knowledge and a healthy skepticism are essential for a data scientist. These traits force you to rigorously test your findings, putting them to the test and looking at problems from different angles. As a data scientist, you will, of course, need to be proficient in programming languages like R and Python. Effective note-taking tools, such as Notion or Obsidian, are also invaluable. They help ensure that insights and ideas are captured and organized, as relying solely on memory can be unreliable.

Read more: Most Popular Programming Languages in 2024

Which of your job tasks do you find to be the most engaging?

Tasks that require research and the development of insights are fascinating to me. I find it incredibly rewarding to create predictive models that provide new insights and drive decision-making. These tasks make me think deeply about the “why” and “how,” pushing me to explore the underlying causes and implications of the data. It's exciting to see ideas or insights brought into the world that have not existed before, knowing they can significantly impact business strategies and outcomes.

How to cultivate a growth mindset

Stanford professor Carol Dweck coined the term “growth mindset” to describe individuals who believe their success depends on the time and effort they expend. It’s often used to describe people who self-instruct, taking the initiative to advance their careers, crafts, or expertise. Key characteristics of someone with a growth mindset include perseverance, a willingness to accept challenges, and an openness to learn from criticism. 

“Remember that experience is often gained through making mistakes. Learn a growth mindset rather than perfectionism. Also, consider more internships at different companies in various locations. Finally, share the valuable lessons you learn along the way.”

— Sina Nazeri, IBM senior data scientist and AI engineer

What three key things have made this stage of your career possible?

First, effective communication and a genuine interest in people have been instrumental in my career. These skills help build strong relationships, facilitate collaboration, and ensure that ideas are clearly conveyed and understood. Second, maintaining a growth mindset; embracing lifelong learning, and being open to new experiences have allowed me to adapt and thrive in a constantly changing industry. Lastly, being engaged in my tasks and project goals. I always ask why a task is important and how I can excel in fulfilling it for more effective and meaningful contributions.

How do you continue learning and growing in your field?

I have a daily markdown note with two key sections: “What made you happy today?” and “What did you learn today?” Reflecting on what made me happy helps me identify the aspects of my work that I am passionate about, guiding me toward projects and tasks that align with my interests and strengths. It also helps me internalize new knowledge and skills daily. For example, if I encounter a challenging data set and learn a new data-wrangling technique to handle it, I note it down. Over time, this habit has built a knowledge base I can refer back to. This is particularly useful in large projects where I might need to recall specific techniques or insights gained over time.

Anything else someone just starting should know?

Don't underestimate the power of networking. Join data science communities, attend conferences, and connect with professionals in the field. Building a strong professional network can open doors to new opportunities and provide valuable insights into the industry.

Learn more from Sina Nazeri and the IBMers

Interested in transitioning into the data science industry? Take the next step in your career with support from the experts at IBM by enrolling in the IBM Data Science Professional Certificate program. You’ll learn foundational concepts like predictive modeling and data visualization in about six months, no previous experience required. 

Those looking to advance their existing skill set may instead earn IBM’s AI Engineering Professional Certificate, in which you’ll build upon your existing data science skills with more advanced concepts like computer vision and deep learning. You’ll also gain practical experience using industry-standard tools like Sci-Py and Tensorflow.