From Industrial Engineering to Data Science: A Non-Linear Journey

My history of education and careers is so non-linear that I sometimes struggle to keep track myself. My path to becoming a data scientist was filled with twists and turns, mainly driven by my passion for numbers and programming. If anyone is interested, here is the story.

Early Sparks of Interest

The journey started early in childhood. I was a bit of a math whiz, always knowing that I wanted to work with numbers but not knowing what exactly. My interest in programming was ignited at the age of 11 when my friend and I wrote simple games in Visual Basic. We also dabbled in web development using HTML and JavaScript, a language that feels so old-fashioned now. This early experience laid the foundation for my future endeavors.

Educational and Professional Pathways

During high school, I excelled in math and physics, participating in a national contest for the International Mathematical Olympiad but being eliminated in the second round. This setback led me to reconsider my initial plan of studying mathematics and shifted my focus towards an engineering degree that combined computer science and theoretical physics. Though I found myself lost and directionless during my studies, I regained my passion for quantitative subjects and pursued industrial automation in my bachelors degree.

My journey further continued when I completed a master’s degree in industrial engineering. In Norway, this degree is actually called “Industrial Economics” and is considered one of the most prestigious engineering degrees. The degree offers high-level courses such as operations research and linear programming, and I was fortunate to study alongside some of the brightest students and best professors. I also had the opportunity to pursue subjects like machine learning, big data, advanced statistics, and statistical simulation modeling.

Bold Career Transitions

During my studies, the oil and gas industry faced its worst crisis in decades, leading to the abrupt end of my career in that sector. Thankfully, I caught a lucky break and entered the tech industry, landing my first job as a developer based on my newly acquired Python skills. I was fortunate to join a company venturing into machine learning and was given a lot of freedom to work on research and development and any projects that caught my interest.

With each job, my role transitioned from a research and development engineer to a machine learning engineer, eventually moving into the role of a data scientist. My career has been fortunate to coincide with the booming era of data science, where opportunities are plentiful and job offers are abundant. In Norway, this area was largely unknown when I started out, but it has since blossomed with exciting prospects.

Lessons Learned and Future Outlook

Throughout my journey, the flexibility of my master’s program in industrial engineering allowed me to focus on subjects that interested me the most. This included machine learning, big data, and programming. I also had the opportunity to work on my master’s thesis in collaboration with the department of computer science, focusing on reinforcement learning.

Now, as a data scientist, my experience comes from a unique background, blending engineering, mathematics, and programming. My story highlights the importance of pursuing passions and willingness to continuously learn and adapt. Whether starting your own journey in data science or continuing along a nonlinear path, the key is to stay curious and embrace the learning process.

Conclusion: My path to becoming a data scientist is a testament to the non-linear nature of education and careers. It is filled with twists and turns, but the end goal of a fulfilling career in data science is well worth it.