Micron Technology Director, Global People Science & Innovation
Stanford University Graduate Certification, Statistical Data Mining
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How did you get to where you are today? What is your story? What incidents and experiences shaped your career path?

Summarized By: Jeff Musk on Wed Feb 05 2020
Yeah, All all great questions. Um, so I So my career path is pretty, um, pretty interesting. Maybe somewhat unique. Um, so if I just kind of step back several years and kind of go back to my university days, Um so just for some background context, I studied psychology as an undergraduate, and, um, I quickly realized at least for me that, you know, going the more political psychology kind of, ah, therapist route probably wasn't for me. Um, I was always kind of its aim or or quantitative in nature, quantitatively oriented. And so I wanted to go the route that around that took me in that way but such that I could still kind of oh, use psychology in my day to day work. Um, so I ended up taking a course called Industrial Organizational Psychology. And, um, it turns out that that branch of psychology is tends to be more quantitative, um, corporations, enterprises, organisations, especially today, even more so than back then, when I was an undergraduate, tender, like using data toe informed decisions and decision making, especially about the workforce. So that's what industrial organizational psychology is really all about Is leveraging data about people to understand people's behavior in the workplace. I understand. You know how they how they think, how they interact. Um, you know what makes them productive and successful in the workplace? How do we optimize people's careers all using data? And so, um, you may have heard the term kind of bubbling up recently, the term people analytics and really what that is, it's, uh it's basically HR analytics, and we have a lot of data around employees in the workplace now, so that makes makes you know what I do. Pretty fun. So, um, so I I end up going and getting an advanced degree in industrial psychology. And then, um, you know, a few years after that is when really kind of like the big data and data science we started take over pretty much everything we do. Um, you know, when that tipping point when kind of we transitioned from desktop to doing mostly everything on mobile devices and applications, You know, it really took me toward that direction. So I ended up getting a certificate and data science, Um, uh, from Stanford. And then so today I'm really able to blend both the best world you know, the best of psychology and the best of data science. And where I said today is kind of the intersection of of those two so jumping ahead to where I am now, Um, I met Micron Technology. I've been at Micron for two years now. I lead the People Analytics team here. My crown. Before Micron, I was at Facebook for four years in Menlo Park, California. Working on a very similar team was 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 ah, my my team there was the fastest growing to you. So again, this idea of people analytics and using data in the workplace to study workplace behaviors and outcomes is just really ah, really popular notion and thing to do right now. So, um, you know, like I said, I really enjoy the work that I'm doing. And those were kind of the experiences and educational types of background and trainings that I've taken to get to the point where you know,

What are the responsibilities and decisions that you handle at work? Discuss weekly hours you spend in the office, for work travel, and working from home.

Based on experience at: Director, Global People Science & Innovation, Micron Technology
Summarized By: Jeff Musk on Wed Feb 05 2020
Yeah. I mean, we also here, uh, we often hear of this concept of work. Life balance. Um, I'd say today, especially in tech companies, that might be a misnomer. It's more like worklife integration. And, you know, we can never get away from this right way we're getting ping for, you know, we get e mails, we get messages, we get calls. All sorts of things were never really completely offline. So we have to do our our best as individuals in today's modern day workplace to take time for ourselves to make sure we're not, you know, getting burnt out in our daily lives. And our jobs especially, um so you know, it's it is important at Micron. We are global company and my team is global. So I have folks, um, dispersed throughout I'm located and Boise, Idaho. But I have team members away from Utah to San Jose all the way to Singapore. Um, and of course, I worked with other teams in India and Malaysia and other locations like that. So I have to be flexible, you know, they're so very early morning meetings that I have to take once in a while with late meetings and after Kate once in a while. So again, it really comes down to myself as an individual to make sure I have time management. Make sure I get some balance in my work. It my home life in my work life, Um, so that that's consistently important. Um, as as a person who's kind of in a leadership role. I lead a team. My team is globally dispersed. Um, you know, part of my my job function is people management. So it's administrative things. It's coaching. It's mentor ship. It's providing direction and guidance and making decisions. And then part of my job is also as an individual contributor, right? So I still am doing things like writing code, putting together presentations, doing things that a non managerial person would also do. So I would say I'm pretty split 50 50 in that respect, but but again, it's important for me to find flexibility in my work on a daily basis.

What tools (software programs, frameworks, models, algorithms, languages) do you use at work? Do you prefer certain tools more than the others? Why?

Based on experience at: Director, Global People Science & Innovation, Micron Technology
Summarized By: Jeff Musk on Wed Feb 05 2020
Yeah, my team and I typically for for our technical work, we use the our platform for statistical analysis. You know, I think it's important for us to be kind of all 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 my grown, that's what the team here was using. That's what my team at Facebook was also using. Um, you know, it's it's free, it's open source. Um, And there, you know, there are good ideas that go on with it like our studio, and you can create really nice dashboards with it, like, um uh, using shiny, for example. So the our platform is very popular. We do a little bit of python. Um, 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, um, span a fairly wide range. I mean, everything from, um from simple linear regression models Thio to generalize that it of models to tree based approaches like random forest. Um, in human resource is again. You know, we're working typically with internal employee data. We don't tend to encounter data sets that are there are millions of rose or billions of rose. So we don't 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. So the company every month And so you know, as those applications final when we get we have very large numbers that we might need to sort through and resumes to parse. Um so So deep learning is useful for things like that as well. So a pretty wide range, Pretty broad spectrum. Um, I would say the most helpful algorithms or ones that are not just good at predicting but also good at explaining because, you know, and workforce Analytics, it's not just, um can we predict if somebody is gonna leave the company. You know, if they're thinking about quitting, that's nice to know. But we also want to know why. Right? So we we tender thio use algorithms that are helpful and inferential on the inferential side as well. Because if someone's leaving? Sure. We could have a conversation with them. Maybe. Or or some sort of intervention. But we want to have that intervention be targeted, right? Is it? Hey, is it because you don't enjoy the work you're doing? You don't have enough variety. You don't like manager, You know which one of those things is it? Or is it all of those things? So that type of information and those types of models are tend to be the most beneficial for us.

What things do you like about your job? Were there any pleasant surprises?

Based on experience at: Director, Global People Science & Innovation, Micron Technology
Summarized By: Jeff Musk on Wed Feb 05 2020
Yeah. Um, I like the fact that, um you know, um, our team tends to get pretty, uh, pretty high visibility. So we meet with, um, you know, we can meet with non managers all the way up to the CEO, and we, you know, that tends to happen regularly meeting a variety of different stakeholders and different levels of people within the organization from V piece down, Thio. You know, other data scientists. And so I think that was one nice surprises. Not every company do. You often get visibility with senior vice presidents and things like that, but But fortunately, we tend to do so. That was the pleasant surprise for me, I think when I got to buy crack.

What are the job titles of people you routinely work with inside and outside of your organization? What approaches do you find to be effective in working with them?

Based on experience at: Director, Global People Science & Innovation, Micron Technology
Summarized By: Jeff Musk on Wed Feb 05 2020
work does tend to be cross functional, because if we're deploying technology, we have to work with the I T department or maybe the knee and, uh, the Enterprise data science team as faras. The core functions of what we do within human resource is, um, you know, we work with people from areas like learning and development, which employs training her out, the company for our employees. We worked with the compensation team. They call themselves Total Rewards, but it's about rewards, recognition and ultimately compensation. So pay, um, equity, things like that. We worked with no leadership developments. We have a team that focuses on continually developing our leaders to make sure we have leaders that are providing the care and resource is that they they need to provide to their teams throughout. Um, and let's see, there are a number number of other teams that we work with, both within within the company as faras working with teams outside of the company. It's typically vendors that we have the provide technology. So, for example, we run a lot of employee surveys. We we use an external employee survey tool, and we often times need to customize that depending on what the survey is, so we have to work with external vendors like that to ensure that you know, we're playing the technology that they provide in such a way that's appropriate.

What major challenges do you face in your job and how do you handle them? Can you discuss a few accomplishments?

Based on experience at: Director, Global People Science & Innovation, Micron Technology
Summarized By: Jeff Musk on Wed Feb 05 2020
sure. Um so I'd say as far as major challenges, you know, our audience is a is a wide range, as I've described some of the folks we work with, and oftentimes they're they're non technical individuals and even leaders who are or non technical in nature. What we do is very data science oriented, very technical. So we have to really know our audience. And, um, you know, this is true across data science teams, not just a team like mine, but pretty much any data science team these days. You not only need technical skills, but you also need Thio be able to translate your work in a very non technical way because, um, you know we can do amazing things. He's amazing out rhythms, the latest greatest technology. But if we can't explain it to you, our company's leaders, and to our partners in such a way that they can understand and go and do something with it, then it's not gonna go anywhere. So we have to really know our audience and be ableto thio. Explain what we're doing in such a way. That's digestible Thio somebody who's not in data science. Oh, so that's that's certainly a challenge that we face. Um, as far as accomplishments, Um, you know, I mentioned that one of the big areas of focus for the company is developing its leaders, making sure we have great leaders for the future company. Um, and, you know, great leaders translate into great teams, and so we want to continue that trend. One of the accomplishments that we have is my team has built a digital leader adviser. So there are different modules in this adviser which can, uh, and say produce insights to leaders that they may not have otherwise known about. For example, um, the adviser that we don't, um, can surface people who might be ready for a promotion. Maybe you haven't considered this individual for a promotion in a while, or maybe they're ready for another development or growth opportunity. Um, maybe have a conversation with them and scope out of development plan. Um, I already mentioned flight risk. That's another thing. That's that's a separate module in this. So if somebody is thinking about leaving the company, we can kind of paying the leader and say, Hey, we were concerned that that so and so on your team might be at risk for leaving the company. You're a few reasons why if you have a conversation with that individual and and make sure they're they're enjoying the work. Um, so this leader adviser is kind of based on nudge theory. If you're familiar with that at all, where we're kind of nudging leaders meeting, we send him an email or a message and say, Hey, there's something here that that might be of interest to you And then we can take them Thio, a portal where they can go in and dig a little bit deeper. So that's one big accomplishment where we can showcase a lot of the different types of work that we're doing in one gun Central Plateau.

What was the hiring process like for your job? What were the roles of people who interviewed you? What kind of questions were asked?

Based on experience at: Director, Global People Science & Innovation, Micron Technology
Summarized By: Jeff Musk on Wed Feb 05 2020
the hiring process was, um, um was multiple stage hiring process. So as typical, it begins with a recruiter. Have a conversation with them on dhe. Usually that initial kind of recruiter phone screen is to determine from both sides, you know, would this be a good fit both from a company level and from a job level? Does this seem like something that be interesting for for the candidate? Um, And then, you know, if that goes well, uh, at least for me, it went thio a conversation with someone on the team that allows them to maybe assess some of the skills and also for you to ask them questions and again further get a sense of whether the job would be a good fit. And then from there, it went to the hiring manager. So the manager of the team, the leader of the team, I got to have a conversation with that person. Um, you know, that kind of gives you a better sense at a at a higher level. You know? Whats what's the team about? What's the vision? What's the mission? Um, and these were all you know, typically phone screens and phone interviews And if all of those go well, a CZ they did for me in this case, the next step was to come on site. So that involves getting a tour of the facility, the office, And then, of course, meeting with other people that maybe you haven't spoken with yet over the phone. Um, so cream. My own site interview consisted of a couple of interviews with people are already on the team since my team has a preexisting to you. Um, there was some questions for me. There were some questions about coding. There were some questions about data, science and algorithms. Um, and then I also had a chance to meet with a couple of leaders. So I met with with my my manager or my future manager at the time, And then I met with actually a senior vice president of HR. So I was fortunate to get with to meet with a senior leader of the company as well. Um, so again, it was more of a question and answer type of format. Nothing really unexpected. Um, I mean, here, uh, these days, sometimes you hear about like, uh um, like, video interviewing. And that could be a very different format. I would say the format that I encountered was was more traditional in a church, and you tend to see that still what?

What qualities does your team look for while hiring? What kind of questions does your team typically ask from candidates?

Based on experience at: Director, Global People Science & Innovation, Micron Technology
Summarized By: Jeff Musk on Wed Feb 05 2020
Yeah, I'd say, um, when we hire ah, new data scientists, um, we probably index around 60% on the technical. So can in the code do they know? You know, do they know the foundations and fundamentals of data science algorithms, especially ones that we tend to rely on? Um, you know, And then I'd say the next part of that is what I alluded to before. It's not just can you do the technical work, but can you explain what you're doing? And can you explain it in a non technical way or in a way that a non technical person could understand? So that's probably the remaining 30 to 40% of what we would tend to assess for a potential future new hire.

What are some future career path(s) for you? What skills, certificates, or experiences do you plan on acquiring?

Based on experience at: Director, Global People Science & Innovation, Micron Technology
Summarized By: Jeff Musk on Wed Feb 05 2020
for my team in either. A couple of things I mentioned that, you know, deep learning is in an area that we we tend to rely on a whole lot. But, um, you know, we as time goes on, we tend to get flooded with more and more data. So we're getting larger data sets as we get better at collecting data. S O. I think deep learning is an area that we're gonna be focusing on quite a bit more. Um, and then secondly, um, N O. P is probably the second biggest area that we're gonna be focusing on. And there's some overlap with deep learning in that space, too. But natural language processing because an HR, we're running things like employee surveys. We get data that our own structured like resumes. Um, there's external data that we can pull in like data from Glass Door or Lincoln. And a lot of this is text based data which were fortunate because not a lot of data science teams necessarily have tax data from humans from people like, for example, in our manufacturing area that our data science team there is looking at machine related data. They're looking at defect product defects and trying to pick things like that. It's not text or or language based, so we're fortunate to have that as part of our values stream. And so those are probably the two biggest areas and skill sets and and experiences that we're gonna be having for my team that are ramping up in the next, I'd say 123 years.

What are different entry-level jobs and subsequent job pathways that can lead students to a position such as yours?

Based on experience at: Director, Global People Science & Innovation, Micron Technology
Summarized By: Jeff Musk on Wed Feb 05 2020
Yeah, I think there are a number of different pathways you can get if if data scientist is your ultimate and goal or leading a data science team, Um, you know, I have an individual on my team that just started as an analyst where, you know, they're putting together some basic reports, you know, starting inside of excel and running pivot tables. And you know they want to take it to the next level and way, provided the resources and training opportunities for that person to acquire additional data science skills to become eventually a full data science. So I'd say, Really, what you need is just if you're interested in data, if you have a passion for or data or data science, you know there's nothing wrong with with starting with the basics. And that's every day two scientists started with the basics basics. At some point, you know, I didn't start Personally, I didn't start coating um, until 2012 and I had already been out of graduate school by that time, and I just was interested in it, and I decided to start coating. So I did some self learning along the way and I'd say especially in data science. The field moves so fast that there's always gonna be some element of self learning. So it's not just that you need, Ah, formal education or even a formal certificate. Um, in all cases, sometimes it's just about going out and learning python or learning are on your own, Um, and kind of taking it from here.

What were the responsibilities and decisions that you handled at work? Discuss weekly hours you spent in the office, for work travel, and working from home.

Based on experience at: People Analytics Manager, Facebook
Summarized By: Jeff Musk on Wed Feb 05 2020
more thio. The types of things that I do it micron. Like I mentioned at Facebook, I was on a very similar team there, the People Analytics team. So again, it was in large part voting, putting together, um, putting together recommendations and results for stakeholders. Oftentimes again, non data science stakeholders and, um, communicating out results in providing actionable recommendations. Uh, and then as as, ah, leader of the team there, of course, there was the part where it's helping the team develop and grow, making sure they have what they need to do. Quality work. Um, so, yeah, Facebook. For me, it was It was very similar to hear it Micron as well. One aspect of of data science that I should call out, Um, just to give you a realistic preview of the job. Ah, big part of our job is wrangling, data, cleaning up data. Um, before we even get it into into our models, we've got to make sure our date is clean and, um and, you know, ready to actually put into the software because if it's not clean, it's sort of the garbage in garbage out type of thing, which we want to avoid at all costs. So we we do spend a lot of time, probably more than half of our time cleaning up data, doing feature engineering and and all that. So I think that's an important part of the data science career that isn't isn't always the funnest to talk about, but it's so so important to get that piece right.

What was the hiring process like for your job? What were the roles of people who interviewed you? What kind of questions were asked?

Based on experience at: People Analytics Manager, Facebook
Summarized By: Jeff Musk on Wed Feb 05 2020
the hiring process, at least for data scientists. Their Facebook was very sick, similar to the hiring process here. Micron. It's right. You start with a couple of phone screens or phone interviews to assess the fit for the role and for the team. And then from there it tends to be an on site visit where again, you're probably meeting with some technical folks and they're gonna ask you some coding questions. Um, uh, you can probably expect, like, ah, white or type of simulation, where you get a somebody asks you how you encode something up, and then you pop onto the white board with a marker and actually start writing out some pseudo code. And usually they're not interested in is the code execute a ble? If I were to take that code that you just wrote on the whiteboard, would that work inside of my machine if I plugged it in? It's usually more about the process, the thinking process and how you got to to where you are with with your code. So I would I would be less concerned about being perfect with the code and more just more concerned with the process. The thinking process and how you would go about, uh, thinking through a problem. Um, so there's the coding piece. There's the question answer piece. Um And then, of course, there's the opportunity to me, too. Usually meet with a couple of leaders and ask them, You know how they run the team? What's the vision for the team, and will they see the team going?

How did the program prepare you for your career? Think about faculty, resources, alumni, exposure & networking. What were the best parts?

Based on experience at: Graduate Certification, Statistical Data Mining, Stanford University
Summarized By: Jeff Musk on Wed Feb 05 2020
Yeah, it was, um It was a great experience. I wouldn't be where where I am today. Hadn't had I not gotten that certificate. Um, the Stanford certificates tend to work like this. Um, you know, it's, um it's it's technically available such that people who are working full time like I was can do the certificates. But they put you in the same classes as the matriculated students, the full time graduate students. So the classes are are no, um, you know, they don't make them easier for people who are working full time. So it is. It is a lot of work, especially if you're working full time already or if you're enrolled in another program somewhere else and doing this kind of inside. It's definitely a lot of work, but it's and it's important to understand. Um, you know, not just not just using the race car, but how to build the race car, and that's that's what they were all about. And that program is teaching how to build a race car, and, um so I think that was the best part of it. For me is understanding the science behind the data science, and, um, you know, I've taken that with me throughout the roles that I've been in the workforce is that

How did the program prepare you for your career? Think about faculty, resources, alumni, exposure & networking. What were the best parts?

Based on experience at: Master of Science (M.S.), Applied Psychology, University of Baltimore
Summarized By: Jeff Musk on Wed Feb 05 2020
Yeah. Um, So my master's degree was again applied psychology with a focus on industrial psychology. And, um, again, why I like that was it was more quantitative in nature. Um, you know, there, there's saying both sides to it. There's the the organisational side which is a little bit fluffy, airy, but you have to learn the fundamentals of bridging psychology in the workplace. So that's things like studying leader leadership and what motivates people in the workplace and understanding howto how to design teams and programs and things like that. And then there was the technical side, which is the more industrial side of things and quantitative side of things, which is what I would say. I enjoyed a little bit more. But, you know, for me, it was important to bridge both and, um, you know, there having having the opportunity to work on projects with other people, you know, other people in that in those courses was important for me because everything that you d'oh in the workplaces is typically team based. I mean, we all have our individual tasks and projects, but at the end of the day, you're typically part of a larger team, and you need to all be working together in the same direction