Curious about landing data science interviews (and ultimately, a job) in Seattle? Today, we’re diving into the Seattle data science job market, the types of companies that are hiring, and advice about standing out in a crowded pool of applicants. Our panel will share advice from two different perspectives: Nathan Thompson is a graduate of data science bootcamp Metis and has landed three jobs since 2016, while Marybeth Redmond is a career coach who helps nurture the careers of new data scientists in Seattle. They share their wisdom about the best ways to get your foot in the door at Seattle companies (and their advice is applicable to students in any city)!
Marybeth, you've been in the career coaching world for a while, but what do you do at Metis?
Marybeth: I am a full time (Monday through Friday) on-site career coach. I work with Metis students during the cohort. That means:
- One-on-one meetings to understand students’ goals for after the bootcamp
- Delivering career workshops about resumes, LinkedIn, networking, how to search for a job in data science, how to negotiate an offer.
- Bringing in guest speakers who are data science leaders in the local community to meet and speak with our students about what different companies are doing in the data science landscape here in Seattle. The guest speakers are always a lot of fun.
- My big penultimate event is inviting in potential employers to meet our grads at Career Day. It's a chance for our grads to get up and present their passion projects, and then instantly be able to meet, network, and mingle with employers.
- Finally, I work with the students almost more after they graduate, because I am their coach until they land a job.
Nathan, what were you up to before you went to Metis?
Nathan: I had just finished a Public Policy master’s degree, so my jobs in grad school were mostly Research Assistant work for professors (a lot of academic stuff). The quantitative work that I did was mostly around income inequality, and how to calculate it at really local geographies.
I had done quantitative work in grad school and was trying to get a job, but I was unsuccessful throughout the Fall in finding a job where I could grow and work with data. Then I found Metis.
Nathan, did you research or consider other data science bootcamps in 2016?
Nathan: I definitely shopped around, doing my due diligence on the programs. I talked to alums at Metis and heard good things. It seemed like compared to some of the other bootcamps, there was maybe a lower bar for entry and a lot more pre-work. So it was something that I could start right away instead of having to go back and study for a couple of months, and then apply. And it was cheaper than other options.
Data science bootcamps run the gamut from accepting complete beginners who have never used a computer before to students who have a PhD. Metis was perfect because I could start it immediately and get into the workforce.
So you attended Metis in San Francisco, then you moved to Seattle after graduating. What motivated that move?
Nathan: I lived in Portland at the time, and I was trying to find a job in Portland, but it's a much smaller scene, especially for entry level jobs. There'd be 15 jobs I could apply for in Portland compared to 100 in Seattle or San Francisco. I moved up to Seattle because I got hired here after graduating from Metis.
Marybeth, could you describe the job market for data science in Seattle? What types of companies are hiring? Is the job market saturated with applicants these days?
Marybeth: Great question. Like Nathan said, Seattle is a larger city than Portland. Sometimes you'll hear Seattle referred to as “Silicon Valley North.” We do have a tech presence here – Microsoft, Google, Facebook, Amazon, obviously. So we have some really big players who are hiring data scientists and data analysts.
We also have a lot of startups that come to Seattle. It's a great landscape for them. So we have a lot of mid-sized companies that many of our grads have landed at.
I would consider the Seattle market pretty darn hot; there are lots of jobs out there, but it’s competitive. It's not saturated. I do find a number of employers wanting to hire senior data scientists. Often, we find that companies are building the data science team within their company, so they want someone with more experience to come in and help build the team.
Some of our grads hear, "Hey, we're interested, we love your skills, but we want someone to come in with a lot of experience out of the gates." But we've also had a lot of success with companies that are more open to internships or contract-to-hire opportunities. We've been able to work with them to build really good hiring partnerships, have success, and build up that success.
It's a hot market. It's competitive, though. You have to differentiate yourself because you're one of many.
Nathan, from your perspective as a job applicant, is that the job market that you've seen over the last three years? And have hiring managers’ views of you as a “bootcamper” changed over the years?
Nathan: It's hard to say if people are changing, or if I'm just changing as an applicant. The hardest job to get in tech is your first job. After that, I've had three jobs since then and I haven't formally applied for any of them. I just got hired or offered jobs. I haven't had to fill out an application.
I will echo Marybeth’s point that the entry level market is very crowded. Every company wants to hire a data scientist. So jobs are there and the need is definitely there. It's not going anywhere. But at the same time, that first job is going to be a challenge for sure.
So you haven’t formally applied for your most recent jobs – how did that work? What’s your secret?
Nathan: I’ve continued to work with people who have moved jobs and wanted to keep working with me. They'll either reach out or they'll make the referral. That's kind of the warm handshake there. In another job, I was working at a startup and it was acquired by DocuSign. That's another avenue I think that works.
Marybeth, can you weigh in here from a more macro perspective? What types of channels do Metis grads use to get their foot in the door at companies? What’s the most successful approach?
Marybeth: That first job, like Nathan said, is the hardest. The first job still requires a lot of networking and trying to get those referrals. You’ll be meeting people, making connections, and following up to get a warmer lead. At the same time, you have to send out applications and write a good cover letter that stands out.
If you're cold applying, expect to send out a higher volume of applications. You have about a 10% chance of getting a job if you're only cold applying.
For that first job, you have to try a few things:
- Join meetups
- Leverage your alumni!
At Metis, our alumni Slack channel is really strong. That's where I see a lot of our grads reaching out and finding out where are our alumni work, talking with them, having coffee, just learning about what they're doing. Whether that turns into a referral or not, it helps them to really set themselves up, whether it's refining their resume better for that job, or writing a cover letter.
Seattle is a big city, but kind of a small town. You have to make that human connection, get to know people and let that open up more doors for you.
Nathan: I second that thought about alumni. That's how I got the first job that I moved up here for – by responding to messages in the Metis alumni Slack channel. Even before I was at my full-time job, I had gotten contract work through the same channel.
Have you been mentoring other Metis graduates as they graduate in the last couple of years?
Nathan: I don't hear from many people once they've graduated. It seems like they're well taken care of by Metis. But I talk to a lot more people who are considering the bootcamp.
If you can rewind and think about your first job at ECL – what was that job interview like and what do you think actually got you your first job in data science?
Nathan: I did a technical phone screen, and then an onsite afterwards. Because I was living in Portland, I had to drive to Seattle (I was actually like 45 minutes late to the interview)! But the interview was programming questions and talking to team members from different areas of the company.
As far as what got me the job – I got the questions right, eventually, by not giving up on them. Later, I talked to my interviewers and they said that they got a lot of applicants who would get halfway through the questions, hit a challenging part, and give up. Unless the interviewer has stopped you from working it out on the whiteboard, just keep going until somebody calls it quits.
Do you remember any interview questions that stumped you? Or have you found that they're questions that really stump people now that you’re the interviewer?
Nathan: Stumping somebody with a question doesn't tell you much as an interviewer. They asked me something like, “What's the time complexity for a machine learning algorithm? Pick one and describe it." I think I got that wrong.
Pop Quiz: You find out that you have an interview in 24 hours – how do you spend that time preparing for a data science interview?
Marybeth: What I've been hearing a lot from our alums is that they’re able to get their positions because of their portfolio. At Metis, you will graduate with five projects. Four of them, you've built completely yourself.
- First, make sure that you’re able to speak about your projects that you designed and developed here at Metis. Be able to talk about why you chose a specific model, what you would do differently, how you would improve upon it. That's a big area that I make sure people can speak to in an interview.
- Research the heck out of the company and make sure that you know as much as you can beyond that job description. Make sure that you understand their industry and maybe even try to think through what problems around data might they be struggling with.
Obviously, talk to anybody you might know who works at the company and can give you some insight. The job description usually gives you an idea of their focus from a technical standpoint.
Sometimes, the interview isn’t technical. If it's your very first phone call with a company, it might be with HR about why you’re interested in this position, or how you got into data science, or what you’re looking for in a salary. I was a recruiter in Seattle for 20 years before I moved to coaching. I know that you can't just tell me that you can do something, you have to show me that you can do it. And that I think focusing on your portfolio shows the interviewer what you can do.
That's great advice. Nathan, how would you spend 24 hours prepping for an interview?
Nathan: I agree – researching the company would probably be top of my list. Know how they make money, and how you're going to make them more money. If it's going to be a technical screen, do coding work on a whiteboard. Whiteboarding is not really a natural thing, so practice. If you have the time, at least just go through the motions so you're comfortable.
Marybeth: Often, one of our alums gets an interview, and they ask for our help. We have a system in place where they can come in and do mock technical interviews or even whiteboard practice with our instructors. I'll do the mock non-technical interview. That helps.
Nathan, your resume has certainly grown since you graduated from Metis – you started with a contract job, then got a job at HCL, then moved to Appuri. And then Appuri got acquired by DocuSign. But aside from resume changes, how have you grown as a data scientist?
Nathan: I've started working much more on the engineering side. In fact, I don't know if anything I've done in the last year would be called “data science.” It's a lot more data engineering. I think that's probably been the biggest change. When you work at a startup, you wear a lot of hats – you won’t just do data science work. Working at a startup forced me to do a lot more software engineering.
As you work, you end up just knowing so much more about software and computers. Of course, you're just cramming and learning at a bootcamp like Metis at some point, but once you’re on the job, you end up learning so much more.
Was there a specific point in the last three years (since you decided to enroll at Metis) when you felt like a “real” data scientist?
Nathan: I think it was when I got the first business card from my job that said data scientist on it. I was like, "Gosh, I'm a big boy now." I think that was probably the moment. When you start at Metis, you're doing data science work, and you're trying to present yourself as a data scientist. But I think getting the card felt real.
Marybeth, how do you advise bootcamp grads to find a job that they actually love? How does a bootcamp grad figure that out?
Marybeth: Great question. I earn my keep by trying to figure out that balance. If you want to find a job that you love, you have to figure out what that means to you. What's your criteria? As much as possible, you need to peel the onion with yourself.
For that first job, sometimes you just have to get it. It may not necessarily be the one that you love, but it can point you in the direction and get you on that path. Or sometimes we actually do land a first job that we really love, and then things happen: companies get acquired, bosses leave, reorgs happen.
My advice is to identify your criteria and then find the job that best aligns with your criteria. Then go into that job and add value, stretch, grow and learn. Own your career as much as possible. Communicate with your manager about your goals and drive that. Raise your hand a lot for projects and volunteer within your company to stretch yourself.
Sometimes your dream job is the one that you already have and it develops into more. Like Nathan was saying, he's had to wear all kinds of hats. He's been exposed to things that he probably didn't even expect.
Nathan: My advice is to not be as picky with the first job. Just get in the game and get the experience. It'll be a lot easier to get the next job. I was at my first full-time job for eight or nine months before I left. So it's certainly possible.
Do you think that the content of the job or what the company does matters?
Marybeth: It can – for a lot of people it matters a lot. I find a lot of our alumni are trying to do something good with their data science skills.
For those folks, working for the Bill and Melinda Gates Foundation might be really up their alley. Getting back to that first job, I think the industry is important to some people, but I think a lot of people were like, "Hey, you know what? I just really want to get that first job and get that experience and figure out what industry I want to be in if there is a certain industry in mind."
Nathan: I definitely agree with that. And it’s not just the industry that’s important, but also the city/location. If you're willing and able to move to a new city, I think that should be on the table. I think it's good to be as broad as you can for that first job.
Speaking of cities – has Seattle proven to be a good city to help you develop your career as a data scientist?
Nathan: I like it a lot. It's been great. There's a big tech industry here, but a lot of the community is still there. It's not the crazy hub that San Francisco is; it's more livable than San Francisco in terms of costs and lifestyle. It's been great so far for sure.
Do you have any favorite Meetups or groups in Seattle that you would recommend? Where should beginners start?
Nathan: I like PuPPy, which is the local Python group. It's pretty active and strong. They have a machine learning advanced topics group that I attend which is good for just continuing education. To get started, start attending meetups!
Marybeth: I would agree with that 100%. Here are some others:
- New Tech Northwest, which always brings in speakers from different companies and also hosts really relevant career fairs or job fairs.
- The Metis Seattle data science meetup. We host a lot of PuPPy events.
- Seattle Women in Data Science (you don't have to be a woman to attend, they just promote women speakers). I've been attending that myself, and I'm finding some outstanding speakers and neat connections and networking.
- GeekWire, which covers more of the broader tech scene and has a really great job board called GeekJobs.
Nathan is DocuSign based in Seattle?
Nathan: They're headquartered in San Francisco. They were founded up here and it’s still the largest office. Most of the engineering is still up here.
Once you get your first job in data science, how do you get better and become a senior data scientist?
Nathan: There are holes in my knowledge that I could fill with more courses and whatnot. But once you have a full-time job, it is very hard to go home from programming all day and do more side projects and take classes.
DocuSign has sent me to conferences to explore that scene and get into it and stay in touch with data science even if I'm not doing data science work day to day. That's another good way to grow if your company is supportive of it.
Is there any other career advice that you have for folks in Seattle or who are about to do a bootcamp like Metis?
Marybeth: My final advice is to blog, write, and maybe even speak at conferences (like Nathan was saying). Speaking might be hard if you're just getting started, but attending those conferences, blogging, and writing articles is something that really gives alumni a little bit more of a leg up when they're trying to break into data science. So get the word out and don’t be afraid to share your knowledge.
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