It's a difficult one to answer because I don't think I am at a particular place I want to be I'm always looking ahead. But I had somewhat of an unconventional career trajectory and I think it's becoming more and more common, especially in the world of data science, mainly because the profession at the domain of data science essentially did not exist more than maybe 5 to 7 years ago. It's attracting a lot of talent from diverse backgrounds so that people with very different backgrounds are coming into the profession of data science. And also, once they move up the steps in their career path, they interact more and more with some other domains, which means that they have to really accumulate some other skill sets in order to communicate effectively. I come from a physics background in astrophysics background and that domain has lived at an intersection, which is between small data and big data, which is called today. So there are a lot of methodologies that have been developed in that domain, which I used for my own research for Blackhole astrophysics on some domain related research. All those methodologies and skillsets really translated well into what I'm doing today. So it's somewhat of an unconventional trajectory. It's not an engineering trajectory that primarily what we see today is people coming from engineering backgrounds into data science and data management.

I'm wearing several hats in my current position. I'm involved with multiple companies, and my engagement varies from project to project, but I'm typically at a senior adviser level. But also I'm a scientist at heart, I roll up my sleeves and dig into individual cases myself to keep myself sharp, but also because I'm very curious. I want to see the implementation all the way from ideation to execution on the ground. So what responsibilities that I have, it's anywhere between hiring people, executing strategies, offering strategy modifications to larger companies to coding on a daily basis. This is again typical for people who are up in the management we need to see more often than not, they get disconnected from the on the ground execution. They don't code anymore if they have in the past. I tried to set an example by being a hands-on servant leader and sit down with the people in my team and code with them, learn with them, learn from them so that that is a broad range of responsibilities. Mentoring them is also very important to me. Weekly hours, it depends really on the project and where we are. in the latest COVID era, it has been fluctuating back and forth, depending on where we are with the project, it can be anywhere between 60 hours to more than 100 hours per week and being an astronomer working in the midnight or after hours is not foreign to me. I have two kids as well, so I'm trying to make the best out of the evenings and nights. I'm not traveling anymore as much, depending on the project I used to travel a bit. Now, having two kids, one infant, and one toddler I try to stay at home as much as I can. Certainly, the COVID situation helps there.

I use typical toolset. I mean, students should take it out of their minds. Typical keywords that they're looking for to put on their CVS works at an entry-level job but as you move up the steps, what counts is the capacity to learn, especially in data science and data related fields, which is moving dynamically on a daily weekly basis. There's something new coming out. They have to be really good learners. So don't think of a software program framework model. You can just google them and pick the top 10 learn them 60 70%. But everyone, including myself, we use StackOverflow. There are some companies that do online coding, and certainly it helps with being fast. But there's so much stuff coming out, it's impossible for a single data scientist to expect to know them all. Obviously choose p R or python. My personal preference is python different frameworks models depending on which domain and industry you're working at, it might depend by Tableau for visualization of power Bi. all those keywords are relevant and irrelevant. I think what they should be focusing on is rather be a quick learner and execute on the ground.