Metis recently hosted a virtual alumni panel featuring four graduates of their Data Science Bootcamp: Jessica Cox, Data Scientist at Elsevier Labs, Michael Lai, Data Scientist for the Philadelphia 76ers, Andrew Tillman, Senior Consultant and Data Scientist at Booz Allen Hamilton, and Ken Chadwick, a Senior Data Scientist also at Booz Allen Hamilton.
During the panel Metis Director of Admissions, Amy Ramnath, and Metis Head of Careers, Megan Ayraud, explained the admissions process and integrated careers curriculum before leading a conversation with the graduates that covered topics like reasons they applied to Metis, challenges faced and triumphs experienced during the 12-week bootcamp, the post-Metis job search process, their current roles, and more.
Watch below and read the transcribed version of the conversation underneath!
I'm Amy Ramnath, the Director of Admissions here at Metis, where we teach data science bootcamps and evening courses in New York, San Francisco, Chicago, and Seattle.
The bootcamp is a 12-week, intensive, in-person course that helps prepare people for jobs in data science. We firmly believe in providing a practical data science education. With that, we take students through five data science projects, helping them build a portfolio and also helping them to manage data science projects once they graduate from the course.
We make a commitment to our students to help them get skills, get connected, and get hired. When I mention skills, I'm referring to the fact that we have an amazing team of data scientists who teach the bootcamps. They come to Metis with industry experience, which helps make the connection between theory and practical application. Their backgrounds vary from applied physics, computational neuroscience, electrical engineering, pure mathematics, and more. We do admissions on a rolling basis, so we are continuously reviewing and accepting applications for any of our future cohorts at any of our locations.
Throughout this panel, you'll hear a bit more about Metis, about our admissions process, and you’ll hear from our Head of Careers regarding how we prepare you for a career in data science during and after the bootcamp. We'll then introduce you to some alumni who can share their experiences.
OVERVIEW OF METIS ADMISSIONS
What is Metis looking for in a bootcamp student?
AMY: There are six traits that we believe are required to be a great data scientist. The first is having technical skills. If you’ve never programmed before, or programmed and didn't really enjoy it, this might not be for you. Data scientists program in order to access and manipulate data, they engineer in order to aggregate different data sets, and they program in order to build apps or visualize data such that the data becomes useful. Data scientists must also possess statistical thinking. You'll need to work with data that is messy and complex and use statistics to interpret it. You need to be quantitative and analytical. It's not just about producing answers but also questioning assumptions and determining if those numbers actually make sense.
Data scientists must also be strong communicators, both visually and verbally. That's because they act as the translators within their companies. They’re taking the insights that they’ve uncovered with data and then must communicate those findings to different stakeholders who may not understand the technical jargon. We focus on oral and visual presentation skills in each of the students' five projects, so they become comfortable presenting in front of others and communicating their findings.
We also look for curiosity and creativity. A great data scientist in inherently curious as they need to hypothesize, ask questions, and dig into data searching for answers. Sometimes that requires being creative and thinking outside of the box. You might get stuck, but how do you get unstuck? It isn't always an easy solution. The last thing we look for is grit. As I mentioned, you can get stuck. Your code won't work, your analysis won't tell you anything. You're hitting your head against a wall, figuratively, hopefully, but you've got to push through, so grit is essential for any data scientist. We've designed the admissions process to measure these six traits.
What is the admissions process like?
AMY: It starts with the written application, which you submit through our website. The part of the applications is just basic background questions, open-ended statistics questions, and a programming exercise. If you move onto the next round, you'll receive three challenges and you have 48 hours to complete a technical assessment and exploratory data analysis, along with a data science project challenge.
After these challenges, you have an interview with one of our senior data scientists. This is an opportunity for our team to learn more about your background, your performance on the challenges, and to help determine that you're a good fit for Metis – and also, to determine that we are a good fit for you. Once the interview takes place, we have seven days to make a decision on your application. The whole process itself can take between two to three weeks from the time you submit your initial application to the time you receive a final decision.
If accepted, you always have the option to defer to a future cohort. If the time of the next cohort does not really work for you, you also have the option to switch locations. You never know where life can take you, but we have locations in New York, San Francisco, Chicago, and Seattle – so hopefully life takes you to one of those cities.
What background do accepted students generally have?
AMY: It varies, and I think that's a great thing. About 54% of students coming have a Bachelor’s Degree, and about 45% come in with an advanced degree, whether that’s a Master’s or a Ph.D. We see all types of majors ranging from computer science, math, economics, statistics, marketing, or even European history – but the aligning factor with all of these students are those six traits mentioned previously. It’s really all about your technical ability, statistical thinking, curiosity, passion, grit, and communication skills.
Q&A WITH METIS CAREERS
Hi, everyone. I'm Megan Ayraud, Head of Careers at Metis. I manage our careers team across New York, Chicago, San Francisco, and Seattle.
What does the careers curriculum look like?
MEGAN: On top of the very intensive data science curriculum you'll follow throughout the 12-week bootcamp, you'll also follow and participate a very robust and intensive Careers Curriculum. There's a lot of moving parts and there's a lot of pieces that we have built into the 12 weeks that we feel are incredibly necessary to help you get that awesome job after graduating from our bootcamp. We do one-on-one job coaching. You'll sit down with your career advisor several times throughout the 12 weeks.
You’ll sit down and talk about your job search and some of your goals and your plans, and road map your plan for after Metis. We also host several workshops throughout the 12 weeks, and those are focused on some pretty big topics that we feel, again, are really important that you grasp on before launching into the job search. Some of those workshops cover things like interview preparation, salary negotiation, data science career paths, exploring what different roles look like at different companies, for example, data scientist versus data engineer versus data analyst, et cetera. We also cover resume and LinkedIn preparation, tying all the pieces together to make sure you're fully equipped with all the tools you need for your job search.
We also do a lot of mock interviewing. We feel very strongly that it's our job to help get you ready for those in-person interviews and those recruiter phone calls that you're bound to have. We do a lot of practice while you're in the bootcamp, including non-technical interview practice and technical interview practice.
What is the Metis Career Day event like?
MEGAN: We put on a career fair, which we call Career Day, at the end of every bootcamp. It's a really fantastic event when we bring employers in-person to our office and students present their fifth and final passion projects. Not only does this mean you get to present your work, you also get to network with the employers who are in the room, make connections, exchange information, and then set up next steps for interviews afterward.
What happens post-graduation?
The goal is to get you confident and get you connected while you're in the bootcamp, and then you graduate and take that momentum with you into your job search. But the support doesn't stop, by any means, once you graduate. You're actually, in most cases, working more with us after the bootcamp than while you're in it.
And we’ve created ways for you to stay connected to our alumni community. It’s more than a couple of hundred people now, which is amazing. Our alumni are fantastic people working at some amazing companies around the country – and around the world, actually. We also built a platform that we call Employ, and it's a job portal, essentially. You go on, you create a profile, very similar to your LinkedIn profile, and then you're able to search through companies that have indicated interest in hiring Metis graduates.
You can reach out to them directly and share your resume. It provides a curated connection with companies that have a direct relationship with Metis. On top of that, you still have a one-on-one connection with me, and you still have the one-on-one support from your career advisor, and a lot of tools at your fingertips to keep you engaged and to keep you feeling supported.
Does the relationship stop once you get a job?
What's cool is that even when you find a job, let's say you go on and you work there for a year and a half or two years, you're always a Metis alum, and so you can always come back and jump onto that Employ platform and look for a new job and to reconnect.
You're always a part of this awesome community and the support never goes away.
Q&A WITH METIS BOOTCAMP ALUMNI
MEGAN: Can you tell us about your background prior to Metis? Were you in school, were you working, what kind of degree do you have? That kind of overview will be helpful.
ANDREW: As far as professional experience, I have a year teaching overseas in English. For my education, I have a Masters Degree in Computer Science with a focus on AI that I worked on before the bootcamp, and I think that's about it.
JESSICA: Before this, I received my Doctorate in Biomedical Health, so I was a basic research scientist. I got frustrated with basic research, I did a postdoc in population health so it was more epidemiology and bio stats, and that was my first exposure to coding and stats. Everything kind of clicked for me then and I knew I wanted to go into data science, so I took the course and finished in September.
MICHAEL: Before Metis, I was a trader. I worked in finance as an equity options trader. Before that, I taught high school math for a little bit, as well. As far as my education goes, I have a background in math and economics.
KEN: I have a Masters in National Security Studies, and I was working as an analyst, but I have an engineering background. At the time I applied to Metis, I was looking at the data science program at Berkeley, and decided that I wanted to do an in-residence, immersive program rather than the online program that's offered at Berkeley.
MEGAN: Perfect segue, Ken. Next question: when each of you was thinking about your next step in your career transition, what made you choose Metis? Were you considering other bootcamp programs, or were you looking at Master’s Degree programs or online programs like Ken?
ANDREW: My degree in computer science with a focus in AI really related to data science. Next, I wanted to build a portfolio of projects that I could put on my GitHub, because a lot of other projects I’d already done, they were team projects at school, and I wanted to do some individual projects. I also really wanted to spend some time going to Meetups, and things of that nature in New York and just doing a lot of networking. I really wanted to take advantage of the [Metis] career services, and I thought it was much better than most schools.
JESSICA: When I was at Columbia doing my post-doc, that's when I got into coding and analysis, and I knew if I continued on the traditional academic career path, I’d have to write grants and I didn't want to do that. So that's when I learned data science is an entire field where that's what I'd actually be doing, so this felt like the perfect amount of time to really get where I wanted to go. I wanted to stay in New York. I knew I wasn't going to be successful with a self-taught class, so this kind of filled all those requirements for me and I was just really impressed with what I had seen with other students' portfolios and knowing that I would leave with something similar to show employers.
MICHAEL: I was looking to switch careers, and I did some research on data science programs. I was living in New York at the time, and one of my friends pointed me in the direction of Metis. There are a couple programs in New York like it. So, I looked into all of them, but I felt like Metis had a pretty good reputation, and the alumni had landed in some of good roles. I decided it would be a good chance for me to pursue that.
KEN: I actually looked at a lot of programs and my criteria was that I wanted to do an in-residence program. I was looking at a bunch of universities to do either a Master's or a Ph.D. and then it sort of rolled into looking at bootcamps. There were no Ph.D. programs for this at the time. This was about a year and a half two years ago. I looked at the Berkeley program, the University of Maryland has a program, and George Mason. I'm from Washington DC, so I was looking at schools in that area, as well as schools around the country. But, again, I really wanted to do an in-residence program.
I actually had looked at a couple programs in New York for data science, and after talking with Metis, I liked the interaction I had with the team. Also, I'm a veteran, so I really appreciated that they were supporting veterans so, that's really what sealed the deal for me was their support for veterans.
MEGAN: The bootcamp is very intense. 12 weeks, every day, Monday through Friday. Some of the work bleeds over to the weekends and into late nights. What were some of the biggest challenges each of you faced during the bootcamp?
JESSICA: I didn't really have a strong programming background, so I was a little intimidated coming in, but a lot of the pre-work made me feel prepared for it. Again, it was definitely still challenging once I got here, but it never felt unmanageable. [A challenge] was just managing my time, and that's so much of what you'll find at work, too – it’s knowing how long to work on a project, and I think Metis does a really nice job kind of simulating what's going to happen in the real workplace.
MICHAEL: Yeah, the biggest challenge for me was managing my time. There were a few times when I was worried I wouldn't finish my projects in time or I would be working on them to the last moment, and I guess that's part of the real world, too. Just sometimes you get pushed against deadlines and sometimes you have to cut back on the things that you're trying to do and manage your expectations, I guess, versus where you are technically. It's a good learning process in terms of pushing your limits, but that was probably the hardest thing for me.
KEN: I'd agree with Jessica and Michael that time management was a big thing, but one of the other interesting things was that I have a little bit of a technical programming background, but I wouldn't say that it is as strong as it is now – but there were students in the class who did have very strong programming skills, so it was also learning the work, figuring out who to go ask and who to get help from if you had a question. There's a lot of talk about the data science unicorn, and those are very rare. It's really a data science team that you want to find and figure out who you can go talk to. I know in my class, it was hard for some people to ask for help. I know that was an obstacle or a challenge for some people, and also, time is always going to be an issue because it's a lot of information in a very short period of time.
ANDREW: Along with time management, I'm going to say that towards the end, the students could define their own problem and have more freedom over their projects, so picking a direction to go for those projects was rather difficult. You know, if you're given a month to work on your final project and then you go down a direction for a week and then switch, and then do the same for another week, and now you have two weeks and you need to finish and it's just like, you're not really setting yourself up for success. It might be helpful to come in and really study some of the previous projects that students have done because there's certain themes within them. You might then have an idea of what you want to do for some of those projects. I mean, you can obviously change your mind, but I think at the end, with having so much freedom, it was a little unusual because a lot of times you're just being told what to do and this time it’s more like, you do what you want.
MEGAN: Probably the biggest reason people come to Metis is to transition their career and find a new job in data science. I think everyone would love to know what your job searches were like. What were some challenges? Where did you find success?
ANDREW: The careers team really helped me out. Particularly in practicing my elevator pitch and just going through different mock interviews. It took a few months, but ultimately I did go to one of the initial contacts who reached out to me like two weeks or so after the program ended. It was Booz Allen and I ultimately went with the offer like a month or two later.
But, it was not all sunshine and roses. The career team did a wonderful job at really making my resume shine. There were a few times during interviews when I was told that the resume just looked so physically appealing, that as they're comparing it to other candidates, they just want to throw the other resumes in the trash, right, because they just want to look at this one. I wouldn't have thought of that without the career team so that was really helpful. After a few months of practice, it was a lot better, it really was.
JESSICA: I got my job from Career Day. A recruiter came from Elsevier and, like you said, it was a bit of a roller coaster. I was actually approached by them about an interview for a job in Philadelphia but I wasn't able to relocate. I thought that door had closed. I had a few other companies reach out and I did a lot of interviews. A lot of the interviews are technical interviews and those require a lot of work and studying and Metis gets you there, but you really have to put in a lot of work after the bootcamp, too.
I got a lot of tough rejections, but funny enough, Elsevier ended up coming back and said they had another position opening up in the New York office and they really wanted to consider me for it. I feel kind of lucky everything kind of lined up at the perfect time and I think it took me about six weeks after finishing Metis to sign my contract. But, yeah it's certainly not all sunshine, as you said, but it all worked out.
MICHAEL: I had a bit of an unusual career search, I guess. I did two of them in a year. The first one was as soon as I graduated, I started applying and I ended up working at IBM for a little while. I got that job basically through the Metis Alumni Network. There were some guys from a previous class that had worked there and they introduced me and gave me a referral and talked to me about the position. Then the second job search, I guess was more of a perpetual job search. I always wanted to work for the NBA, so even after I had landed a job, I would just send a resume over, or if someone would contact me out of the blue, which actually happened once, then I would try to interview.
In that case, my final project went a really long way for me. One company, the company that does motion tracking for the NBA, actually found my blog one day and emailed me and asked me to interview with them. Then actually, I found this job post through a connection I had made at Metis. It was actually an in-class speaker who set me up with a person who worked with the Indiana Pacers, and they directed me to this opening. I guess, all the networking that happened throughout the course and after the course, it really went a long way in terms of getting a job.
KEN: When I finished Metis, I interviewed with three companies. Two of them were interested in me because of my final project. I accepted a position with Booz. As you go through the program at Metis, think about at passion projects that might help you with employers because that's going to get you noticed. Like Michael said, one company went out and saw his blog and contacted him. My final project was a Twitter real-time anomaly detection using a Twitter feed stream. I guess I was sort of lucky that the Career Day sort of put my project out there. It got seen by a couple companies, they were interested in me and I just eventually just picked one and took the offer.
MEGAN: Tell us about your actual role at each of your companies. What are some of the projects you work on? What does data science look like to you in your professional life?
ANDREW: As a consultant, they have me working on many different projects, so I can't just talk about one. But one of the more interesting ones that I got to work on over the last year was matching up resumes with the job description and gathering data from the recruiters that click around on the internal website to start trying to predict which candidates would be a good fit…I also did quite a bit of data engineering where there was some web scraping involved and cleaning the data, and doing some interesting things with that.
JESSICA: I work within Elsevier’s lab team and we are tapped with identifying research goals within a company that we can accomplish within the next two to five years. I'm not in product development, it's really just corporate R&D, and I am the domain expert on the team since we are a science and medical publishing company. I am the only scientist right now, we just hired one more, on a team of computer scientists and engineers. It’s been really interesting for me to be able to fill that role, where I'm still using my degree but I'm also doing a lot of data science.
Right now the one project that we're pushing really hard on is, we're trying to develop an algorithm to detect duplication within images that are submitted to our journal. There's been a lot of news recently around using AI and Peer Review, so we're really interested in that space and seeing how we can actually detect fraud and not let it go into publication. That's something I'm working on. I also work on a citing census project. More or less, it’s giving a scientist a better understanding of how their papers are being cited by other papers. If there are ways we can identify different biological or biomedical papers as a method paper or, you know, seminal in the fields and so that's all using text into a natural language processing in LT. But, it’s about 50% coding and a lot of it is using my past life as a scientist to try and move these projects along.
Yeah, so I would say most of my time is spent coding and doing analysis and building models and that kind of stuff.
KEN: I'm in a little bit of a unique position. Like Andy with Booz Allen, we're consultants for the government, so I'm the lead data scientist on the team for my client. I have a team of about eight other data modelers and data scientists, and I herd them together and make sure all the wet cats are running in the same direction. I help them out with a lot of their tough programming algorithm problems, but it’s a lot of data engineering that we're doing right now, slowly steering the government into the direction that will facilitate them actually using some data science machine learning capabilities. They're not there yet. We're moving closer, but having someone in to consult with them and advise them on what steps they need to take to get this capability is getting them to move in that direction.
I've been on the client side for about six months now and we're starting to see some movement. It’s baby steps for them, but at the same time, it’s about building a new infrastructure in a knowledge base in their workforce that will enable them to actually do data science as they make the infrastructure and policy changes. It's data engineering, data science, and then a lot of advising on questions like, do we need to change this policy? It’s very interesting and dynamic.
MEGAN: We're going to open it up to the floor to audience questions. I see there is one question for me. Do we work with non-profits?
Yes, we do. We have a very strong partnership with DataKind. We do a lot of events with them. We also have had a grad work at the United Nations, so we also have a relationship with them. I just started working with a company in Seattle, actually, called Providence Health and they have an amazing mission. They're trying to bring healthcare to underrepresented populations. They’re mostly on the west coast for now, but they are expanding and they're looking to expand their data science capabilities. We've done some other events with people and data scientists from the Crisis Text Line and the World Bank. That's a big area of interest for us personally as an organization and a lot of our students and graduates are also interested in deeper social causes.
KEN: If I can add, one of my projects while I was [at Metis], was actually with a couple of nonprofits, helping them, looking at international crisis as a predictor.
MEGAN: Another question, this time for the alumni. Did you have any hesitation to quit your full-time job to attend Metis, and did you have any fear of being unemployed?
MICHAEL: I feel like I can answer that pretty quickly. So, I have quit my job several times. I've had three careers now. First as a trader, then I taught high school, and now I'm a data scientist. I guess in terms of that I was a little bit worried, but I've done it before so I figured I might as well try again. Then in terms of getting a job afterward, I was actually really worried about it. But I think the support from the career services team was really good and they definitely made the process a lot less stressful than it could have been, I think.
JESSICA: Yeah, it’s pretty scary quitting your job. For me, I was on such a prescribed path, where you did your doctorate, you did your post-doc and you became a professor, so to deviate from that was a big deal. But at the same time, the outcomes of everybody else who had gone through it were so successful and everybody I spoke to had nothing but good things to say. It was certainly a scary leap, but I know I would not have gotten the job that I have now if I hadn't done it, and I have zero regrets about doing it. It was essential for me, I'm really happy.
KEN: I guess I come from a bit of a different background. I have a history of quitting jobs and moving in new directions. I look at it as a challenge and an opportunity to grow, and I was in a career path that was stagnating. I wanted to do something different. This was an opportunity to do that in a way that I wanted to do it, which was in person and immersive. For me, I didn't even pause to think about it. It was just like, ok this is what I'm going to do. I went in and told my boss. I didn't ask for permission. They said, "Well, you can take a leave of absence." And then a week before I was supposed to come up here to New York, they said, "Ah, you can't take a leap of absence. You're going to have to resign." Which, again, I had no problem. I wasn't worried about finding a job because right now, I'm in a position to hire, and I can't find even qualified people, so data scientists are in demand. That has set me up very well for success.
MEGAN: Next question. Did Metis prepare you for technical interviews and/or was there more that you needed to pick up in order to do well in the interview?
ANDREW: I think they'll cover some of the basics, but at the same time, I think it's to your advantage to form study groups with the students and work on all the technical questions. It's not just the three months that you're spending at Metis, but the last 20, 30, 40 years of your life history that you're going into the interview with and all the different skillsets that come along with that. But as far as the technical interview process for Booz Allen, I think Metis prepares you very well.
KEN: Metis is the beginning down the road for data science. It's 12 weeks and you can't learn everything there is to know about data science in 12 weeks. It'll get your foot in the door and it's a continuing education process. I go out and try and be current with what else is going on in the data science world every week. Every day, really. Going to Meetups in D.C., talking to people, seeing what they're doing, how they're doing things. I think a lot of it has to do with what you put into it. It's not going to be just Metis that will get you the job.
MEGAN: Next question. Is there anything you would suggest we do to improve our bootcamp?
MICHAEL: I think the biggest thing for me is that I had to learn really low-level computer science stuff on my own. Metis does a pretty good job of preparing you to program and survive in a job and be able to do all the things that you need to do. But for certain jobs, you have to study a lot on your own. I don't know if it's even feasible, to squeeze all that into a three-month course. I don't know if it's necessarily something that needs to be added, but it's something that I think is necessary, individually to further study those kinds of things.
KEN: I think it's tough to say a recommendation to change something because then you're taking time away from something else, but looking back now, I think maybe a little bit more time working in the AWS environment might have been helpful. It would be helpful for stuff that I'm trying to do now with my team.
MEGAN: A question was asked about our job prospects in Chicago, given that it's our first bootcamp this quarter. I’ll answer.
MEGAN: I work very closely with the Chicago Career Advisor there, and it's been incredible to see the momentum there in just a month and half of actually operating our bootcamp. Typically every cohort, we have about 8 to 12 speakers come in from area companies to talk to our students about data science at their company and some of the cool stuff they're doing, job openings, etc. She was able to book 12 speakers, the max, without any kind of hesitation or challenge. We really had a ton of interest just flooding in of companies wanting to get their name in front of our students and start building a presence with our community there.
We also have five or six graduates from earlier cohorts now working in Chicago. They're working at Datascope, they're working Grub Hub, and Capital One Labs, so some pretty big names. It’s an awesome, growing community.
MEGAN: Ken, you talked a little bit about your final project, but can the rest of you talk about what you did?
ANDREW: My project revolved around real estate and the main part was just collecting the data. The website I was scraping from was not really letting me collect the data very easily, and I went with a distributed process so there were many programs that were running together and working with each other and going over the dark web – well not the dark web – but I had to change my IP address and things of that nature so I could just acquire all the data I needed. Once I had that, I built into a bit of a frontend app, where you could play around with data prediction. What I was looking at was some of the input values and how long it was going to take for a house to sell – specifically looking at how elastic the price is. If you're changing the price up and down a little bit, would it affect the time it takes you to sell the house?
MICHAEL: My final project was related to NBA, because I'm obsessed with the NBA, I guess. It was a motion tracking project where I tried to take YouTube videos and find the positions of all the players on the court, and create an overhead mapping of that. If anyone's familiar with Sport View, it was basically trying to create Sport View data out of a YouTube video. It was very challenging, technically. I guess computer vision stuff, it takes a lot of programming and a lot of time. But it was a really cool project and it was very fun. Actually, if you really want, you can check it out because it's still on my blog.
KEN: For my project, I was monitoring a Twitter stream, looking for anomalies in the stream to indicate that an event has happened and then would use NLP to extract features that determine what type of event it was.
MEGAN: One last question. How long after graduating Metis did it take for you to find your job?
ANDREW: I think I officially got an offer within three months. That included the holidays, which took away two weeks or whatnot, because it was around Christmas time. I got it around my birthday in March.
MICHAEL: It took me about two months to find my first job. It was much quicker than I expected, I think.
KEN: After graduation, I took a month off to take care of some business, but when I started looking for a job, it took about 45 days.
MEGAN: Just to add to that, I would say we usually see most people employed within the first three to four months. It also depends on your personal timeline, as well. Some people take time off to travel, or just relax, because remember, this is a very intensive program, so sometimes people take a little bit of time off after to just kind of decompress and see their families and friends again. It just depends on your timeline and how active you are. Each of our grads here today fell within the pretty standard timeline of folks getting jobs.