Philips

I applied for the Philips Data Science & AI Intern role through an off-campus opening. After the initial resume screening, I received an online coding/technical assessment. The first round was a coding test. The questions were mostly around SQL and basic machine learning/data science concepts. The coding part was manageable, but the ML questions required knowing the fundamentals rather than just solving programming problems. After clearing the assessment, I had a one-on-one technical interview. The interviewer started with database concepts and asked about the difference between SQL and NoSQL databases. We then moved toward machine learning fundamentals. One of the main questions was about regularization in machine learning. I was asked why regularization is needed and how techniques such as L1 and L2 regularization help control overfitting. There were follow-up questions around model complexity and the effect of regularization on the learned parameters. The interview was more focused on understanding the concepts than solving difficult DSA problems. I was comfortable with some of the questions, but I wasn't able to answer all the follow-ups with enough depth. The process ended after the technical round, and I was not selected. Overall, the interview showed me that for a Data Science/AI internship at Philips, having good Python/SQL knowledge is useful, but ML fundamentals such as overfitting, regularization and model evaluation are also important.