Pays well, good benefits, somewhat empowered. Good facilities and tools.
The PLM did not know the business and could not make decisions. Indecisiveness led to wasted efforts and was late to market.
The new CEO is chopping the business. Tons of wasted resources were spent on SW architectures that were scrapped, at the expense of programs with viable business. Poor management, conviction, and execution.
Give businesses that are critical to the growth of the company a chance to develop. Nurture and fund them. Some of the businesses that you have decided to exit will end up being a huge mistake and end up costing the company much more to re-enter than continuing investment would ever have cost.
One-on-one interviews, both in person and on the phone, took place over the course of several weeks. The group came down to two qualified candidates: myself and another person. In the end, it was decided to hire both of us, as we have complimentary
There were two interviews: one over the phone and a day at their office. Most of it was validating the profile, with deep probing questions on the projects done. Primarily to get a sense of depth of understanding. There were few programming questio
Three 30-minute rounds focused on DS/Algorithms and ML concepts/project experience. The first two rounds were technical coding sessions, requiring explanation of concepts with examples. The last round was strictly data science/ML with conceptual ques
One-on-one interviews, both in person and on the phone, took place over the course of several weeks. The group came down to two qualified candidates: myself and another person. In the end, it was decided to hire both of us, as we have complimentary
There were two interviews: one over the phone and a day at their office. Most of it was validating the profile, with deep probing questions on the projects done. Primarily to get a sense of depth of understanding. There were few programming questio
Three 30-minute rounds focused on DS/Algorithms and ML concepts/project experience. The first two rounds were technical coding sessions, requiring explanation of concepts with examples. The last round was strictly data science/ML with conceptual ques