My career path is pretty interesting and maybe somewhat unique. So if I just kind of step back several years and kind of go back to my university days, so just for some background context, I studied psychology as an undergraduate, and I quickly realized at least for me that going to more clinical psychology kind of therapist route probably wasn't for me. I was always kind of more quantitative in nature, quantitatively oriented and so I wanted to go the route that took me in that way but such that I could still use psychology in my day to day work. I ended up taking a course called Industrial-Organizational Psychology and it turns out that branch of psychology tends to be more quantitative, corporations, enterprises, organizations, especially today, even more so than back then, when I was an undergraduate, tender like using data to form decisions and decision making, especially about the workforce. So industrial-organizational psychology is really all about leveraging data about people to understand people's behavior in the workplace, to understand how they think, how they interact, what makes them productive and successful in the workplace? How do we optimize people's careers using data? And so, you may have heard the term bubbling up recently, the term People Analytics and really what that is, it's basically HR analytics, and we have a lot of data around employees in the workplace now, so that makes what I do pretty fun. So, I ended up going and getting an advanced degree in industrial psychology and then a few years after that is when really the big data and data science wave started to take over pretty much everything we do. That tipping point when we transitioned from desktop to doing mostly everything on mobile devices and applications, it really took me toward that direction so I ended up getting a certificate in data science from Stanford. So today, I'm really able to blend both the best world, the best of psychology and the best of data science and where I sit today is kind of the intersection of those two. Jumping ahead to where I am now, I am at Micron Technology, I've been at Micron for two years now. I lead the People Analytics team here at Micron. Before Micron, I was at Facebook for four years in Menlo Park, California, working on a very similar team as one of the co-founders of the Facebook People Analytics team and when I left that team at that time, we were the fastest growing team at Facebook and Facebook now has about 40,000 people and my team there was the fastest-growing team. So again, this idea of people analytics and using data in the workplace to study workplace behaviors and outcomes is just a really popular notion and thing to do right now. So, as I said, I really enjoy the work that I'm doing and those were kind of the experiences and educational types of background and training that I've taken to get to the point where I am now.

We often hear this concept of work-life balance, I'd say today especially in tech companies, that might be a misnomer, it's more like work-life integration and we can never get away from this, we're getting pinged, we get emails, we get messages, we get calls all sorts of things so we are never really completely offline. So we have to do our best as individuals in today's modern-day workplace to take time for ourselves to make sure we're not getting burnt out in our daily lives and our jobs especially. At Micron, we are a global company and my team is global so I have folks dispersed throughout I'm located in Boise, Idaho but I have team members all the way from Utah to San Jose all the way to Singapore and of course, I work with other teams in India and Malaysia and other locations like that. So I have to be flexible, there're some very early morning meetings that I have to take once in a while and late meetings that I have to take once in a while. So again, it really comes down to myself as an individual to make sure that I have time management, to make sure I get some balance in my work and my home life and my work life so that's consistently important. As a person who's kind of in a leadership role, I lead a team, my team is globally dispersed. A part of my job function is people management so it's administrative things, coaching, mentorship, it's providing direction and guidance and making decisions and then part of my job is also as an individual contributor, so I am still doing things like writing code, putting together presentations, doing things that a non-managerial person would also do. I would say, I'm pretty split 50-50 in that respect, but again, it's important for me to find flexibility in my work on a daily basis.

My team and I typically for our technical work, we use the R platform for statistical analysis. I think it's important for us to be on the same platform as a team, and that's what we've sort of decided and have used for a number of years even before I came to Micron, that's what the team here was using. That's what my team at Facebook was also using, it's free, it's open-source and there are good ideas that go on with it like Rstudio, and you can create really nice dashboards with it using shiny, for example, so R platform is very popular. We do a little bit of python, there are other data science teams at Micron that probably used python a little bit more so in the data science arena, of course, that's a very popular language programming language to use. The algorithms we use are a fairly wide range. I mean, everything from simple linear regression models to generalize that it of models to tree-based approaches like the random forest. In human resources, we're working typically with internal employee data. We don't tend to encounter data sets there are millions of rows or billions of rows so we don't tend to use deep learning a whole lot. Although there are some situations where we have found that useful, for example, we get a lot of applications to the company every month so as those applications we get, we have very large numbers that we might need to sort through and resumes to parse so deep learning is useful for things like that as well so a pretty wide range, pretty broad spectrum. I would say the most helpful algorithms are ones that are not just good at predicting but also good at explaining because workforce Analytics, is not just, can we predict if somebody is going to leave the company, if they're thinking about quitting, that's nice to know but we also want to know why? So we tend to use algorithms that are helpful and are on the inferential side as well because if someone's leaving, we can have a conversation with them maybe or some sort of intervention but we want to have that intervention be targeted, Is it pay? Is it because you don't enjoy the work you're doing? You don't have enough variety or you don't like the manager, which one of those things is it or is it all of those things? So that type of information and those types of models tend to be the most beneficial for us.