I recently had the unexpected opportunity to interview for a data science role at one of the FAANG companies. While I didn’t pass the phone interview, the experience was invaluable and gave me a lot to think about.

The process itself was insightful. The interviewer was professional and clear about the recruitment and interview structure. As we delved into the technical assessment, I found myself realizing how much I genuinely enjoy data science. I’ve missed working on data preprocessing, model training, fine-tuning, and evaluating model performanc - tasks I haven’t had the opportunity to focus on in my current role.

My present position involves developing and enhancing chatbot systems and clinical tools, as well as deploying applications that benefit the healthcare sector. This work has been incredibly rewarding and has given me practical, hands-on experience that I deeply value. However, it’s become clear to me that I still have a strong passion for core data science work.

During the interview, I also took the chance to ask for general feedback, which I found incredibly valuable. Here are the key takeaways:

1. Broaden Industry Exposure

The interviewer suggested that I explore opportunities outside of the healthcare industry. While healthcare has been a fulfilling domain for me, broadening my horizons could provide diverse experiences and new challenges that enhance my skills. This aligns with advice I’ve received from peers as well. At this stage in my career, exploring different industries could be pivotal for growth and development.

2. Strengthen MLOps Knowledge

While my data science foundations are strong, the interviewer noted that I could improve in areas related to model deployment and monitoring - essential aspects of MLOps. He emphasized the importance of understanding both the theory and practice of MLOps, even in small-scale projects. Leveraging cloud services for low-cost experimentation was also recommended, as it could bridge the gap between theoretical knowledge and practical application.

3. Dive into Generative AI and LLMs

Given the rapid advancements in Generative AI and large language models (LLMs), the interviewer encouraged me to deepen my knowledge in these areas, especially as they align with my interest in NLP. Learning to fine-tune LLMs and applying them to personal projects could be a meaningful way to gain hands-on experience and stay relevant in the field.

These insights have given me a clearer path for growth. While I am proud of the work I’ve done so far, I am excited to focus on improving my skills in these areas, tying them into both professional and personal projects as I move forward.

This feedback is now a guiding framework for my next steps in becoming a more well-rounded data scientist.