From Chemical Engineering to a Data Scientist in the US Tech Industry: Hoda Noorian's Journey

Meet Hoda, a dynamic data scientist who went from studying chemical engineering in Iran to thriving in the US tech industry. Her journey includes founding a startup, working in venture capital, and mastering data science.

Discover her practical advice and lessons for anyone looking to enter or transition within the tech field.

Note: We held this interview on December 9, 2023.

I began my undergraduate studies in chemical engineering at one of the top universities in Iran. However, I quickly realized I wasn’t interested in the coursework or the industry’s future.

Struggling to find relevance in my studies, I was introduced to entrepreneurship at age 19 or 20. I co-founded a startup called Barx, which aimed to be an “Uber for the internet.”

This venture exposed me to the startup ecosystem, and we even attended international conferences. Winning a conference in Berlin called IBridges earned me a scholarship to study entrepreneurship at UC Berkeley during a summer course.

This opportunity brought me to the United States.

Upon arriving in the US, I initially planned to pursue an MBA but soon decided I wanted to stay technical. I pivoted my focus and started taking prerequisite courses in computer science, linear algebra, and advanced statistics to prepare for a data science master’s program.

During this time, I worked as a venture capital analyst for almost two years, gaining valuable experience evaluating products and technologies.

I was accepted into the University of San Francisco’s data science program with a substantial scholarship.

While there, I interned at Airbnb, where I worked on the ethical implications of experimentation. My role was machine learning-heavy, involving projects like building a model to infer gender from various socio-economic factors.

This experience also included educating other data scientists on the ethical use of such models and conducting studies to identify potential biases in past experiments.

Graduating during the onset of COVID-19, I found the job market challenging, but it felt like the right time to join a healthcare company.

I joined Carbon Health as one of their early data scientists and stayed for three and a half years. At Carbon Health, I led growth experimentation efforts, focusing on A/B testing and understanding user friction.

This role involved high levels of ownership and responsibility, working closely with executives to align data science initiatives with company goals. I briefly served as a data science manager but returned to an individual contributor role after organizational restructuring.

A month ago, I joined Notion, excited to embrace new challenges and opportunities in a different sector.

When I became a data scientist manager at Carbon Health, one of the main challenges I faced was navigating the company’s restructuring during a period of layoffs.

This created an environment of uncertainty and required me to manage a smaller team with limited resources. Another challenge was ensuring the high ownership and responsibility expected from our data science team.

I had to balance leading the growth experimentation efforts, which involved complex A/B testing and user friction analysis, while also aligning with the company’s top priorities. The role required a deep understanding of both technical and business aspects to effectively communicate and collaborate with executives.

What were your responsibilities, and what did you learn from leading growth experimentation?

My responsibilities included designing and executing experiments to improve user acquisition, retention, and conversion rates.

I collaborated closely with product managers to identify key metrics and formulate hypotheses for testing. One significant learning was the importance of clear communication and collaboration with cross-functional teams.

I realized that convincing PMs and stakeholders about the value of data-driven decisions required not just presenting data but also providing actionable insights and alternative solutions.

This approach helped build trust and drive impactful changes within the organization.

I decided to move to Notion for a few reasons. First, after spending over three years at Carbon Health, I felt that I had gained valuable experience and learned a lot, but I was ready for new challenges.

At Carbon, my role was heavily focused on conversion optimization and user experience within the healthcare domain, which eventually started to feel limiting. I didn’t want to be pigeonholed as just a “healthcare data scientist,” so I looked for opportunities that would allow me to diversify my experience.

I was particularly drawn to Notion because of the company’s potential to build an AI-connected workspace. Notion stood out because they have what it takes to integrate user knowledge, good documentation, and advanced tools to create a seamless and automated user experience. The quality of people at Notion was also a significant factor.

The team was not just talented but consistently impressive across the board, which was exciting to me. I wanted to work with and learn from such a high-caliber team. Additionally, the innovative nature of Notion’s product and its growth trajectory were very appealing.

My experience with the interview process at Notion was very positive.

The process was fast and well-organized, which made it stand out from other companies I interviewed with, like DoorDash and PayPal. The recruiter at Notion had reached out to me a couple of years before, but the timing wasn’t right back then.

When I was ready to move, I reconnected with them, and the interview process took about one and a half months in total.

To prepare for the interviews, I focused on brushing up my SQL and Python skills, particularly using pandas for data manipulation. I practiced solving product questions and revisited my notes and coursework on A/B testing and statistics.

Interview Query, in particular, provided structured guidance and practice questions that helped me solidify my understanding and approach product data science questions effectively. I also relied heavily on Emma’s videos on product data science questions, which provided a great structure for approaching these types of problems.

My preparation was less about mastering the most complex topics and more about ensuring I had a solid understanding of the fundamentals and could apply them effectively.

One challenge during the interview was the technical phone screening. I was asked to have my notebook ready to share my screen, which initially stressed me out as I wasn’t sure what to expect.

However, the questions were straightforward, involving SQL and Python tasks that I was comfortable with.

Notion’s interview process also included two rounds of behavioral interviews. They were rigorous and focused on cultural fit, which I appreciated because it showed how much they value their work environment.

Some of the behavioral questions were tough, such as describing the worst manager I had or the cultural factors I disliked in previous jobs. These questions required deep reflection but ultimately helped ensure alignment with Notion’s values.

Overall, my preparation was thorough and targeted, focusing on practical skills and fundamental knowledge, which helped me feel confident and perform well during the interviews.

There are two significant lessons that have profoundly shaped my professional philosophy:

Understanding the Difference Between Education and Work

When I started my career, it took me some time to realize that work is very different from education. In school, there is usually a correct answer, and someone has it.

At work, no one has the correct answer, and your manager isn’t necessarily testing you—they often don’t know the answer either.

Understanding this distinction was crucial for me. It taught me to be more independent, to trust my judgment, and to be proactive in finding solutions.

Finding the Overlap Between Personal, Team, and Company Success

Over the years, I’ve learned that true success comes from understanding where your personal goals, your team’s objectives, and the company’s mission overlap.

When you can align your work with what’s important to your team and the company, you create a significant impact. This alignment not only drives results but also brings personal satisfaction and a sense of purpose to your work.

These lessons guided me from chemical engineering to startups, venture capital, and data science. They kept me focused and motivated, and I hope they inspire others in their careers.

For those looking to enter the tech field or transition within it, my advice is to stay flexible and continuously seek new learning opportunities.

My journey began with a degree in chemical engineering, which I found uninspiring and unaligned with my interests. The turning point came when I co-founded a startup called Barx and won a scholarship to study entrepreneurship at UC Berkeley.

This experience exposed me to the dynamic world of startups and venture capital, where I developed a strong product mindset. Transitioning to data science, I took foundational courses in computer science, linear algebra, and statistics while working as a venture capital analyst.

Always be open to learning and adapting, leveraging available tools and resources to enhance your skills and knowledge. This combination of technical education, practical experience, and a willingness to pivot when necessary is the key factor that has led me to where I am today.