Michael’s journey from a mechanical engineering background to a thriving data science career illustrates how determination, hands-on learning, and adaptability can help anyone break into this dynamic field.
Q&A: Michael’s Data Science Journey
Q1: Can you tell us a bit about your background and how you started your journey toward Data Science?
I graduated in Mechanical Engineering in 2020 and worked for about 1.5 years as a supervisor in inventory management at a CNC-based company. Wanting a new challenge, I shifted my focus to data science and enrolled in a course with CCI (Center of Computer Intelligence) around September. My mechanical background fostered innovative thinking, which has been valuable in this transition.
Q2: How did you prepare for your interviews?
Rather than just practicing traditional problems, I focused on real projects. Hands-on experience was crucial, and working on machine learning and deep learning projects gave me the practical knowledge needed for interviews.
Q3: How many interview rounds did you go through?
There were six rounds:
- Mindset and aptitude testing
- A technical project (Machine Learning, Deep Learning, NLP)
- Communication and behavioral interview
- Two technical interviews
- A final discussion with senior leadership
Q4: Can you walk us through the six rounds of interviews you experienced?
The first round tested mindset and aptitude with psychometric tests. The second round was a hands-on technical project with deployment. The third focused on communication and explaining technical concepts. The fourth and fifth were deep technical dives into my projects. The sixth was a discussion with directors and VPs about my technical and professional approach.
Q5: What advice would you give to others preparing for Data Science interviews?
Practice is vital. Understand your code and approach—don’t just copy solutions. Debugging skills are crucial, as small errors can cause big delays.
Q6: What specific skills should candidates develop for success in Data Science?
Beyond technical skills, problem-solving and creativity are essential. There are often multiple ways to approach a problem, and creative thinking can set you apart.
Q7: How do you recommend building a portfolio of Data Science projects?
Select interesting problems, work on them from scratch, and document your journey. Make sure you understand every part, from data cleaning to model deployment.
Q8: There’s a concern about AI taking over jobs. How do you think Data Science professionals will fare in the future with this trend?
AI is automating many jobs, but data science professionals remain in high demand. Human intelligence is still needed for problem-solving, model tuning, and real-world applications. The growth in AI and machine learning is actually increasing opportunities for data scientists.
Q9: Will AI impact job availability for Data Scientists?
While AI automates some tasks, it creates more opportunities for data scientists. Human oversight, creativity, and domain-specific knowledge are needed to solve real-world problems.
Q10: What about people entering Data Science from non-technical backgrounds, such as Finance?
It’s very possible. A strong foundation in mathematics and statistics helps, and learning Python and machine learning theory is manageable with consistent effort.
Q11: Do you need to know Python to be a Data Scientist?
Yes, Python is essential. It’s the main language used in data science and is relatively easy to learn. Start with basics, then move on to libraries like Pandas, NumPy, and Scikit-learn.
Q12: As a final piece of advice, what would you suggest for someone starting out in Data Science?
Stay curious and never stop learning. Practice regularly, work on real-world projects, and understand the theory behind the algorithms and techniques you use. Focus on both coding and the reasoning behind it.
Insights from Michael’s Path to Data Science Mastery
- Transitioned from mechanical engineering with no prior IT experience.
- Gained practical skills through hands-on projects and real-world problem-solving.
- Developed a portfolio that demonstrated genuine project ownership and technical depth.
- Navigated a rigorous six-round interview process, including technical, behavioral, and leadership discussions.
- Emphasized debugging, communication, and stakeholder engagement skills.
- Proved that non-technical professionals—including those from finance—can successfully enter data science with the right preparation.
- Stressed the importance of staying updated with current AI/ML trends and industry practices.
Michael’s story highlights that success in data science comes from resilience, strategic preparation, and continuous improvement. With the right guidance and hands-on experience, anyone can transition into this rewarding field and thrive—even as technology continues to evolve.
