Madison Myers: Take a marathon approach, not a sprint

Madison has gone through some enormous career change in just the past few years. She is currently a Senior Data Scientist at Change Healthcare, writing machine learning algorithms every day — but she didn’t plan it that way. We’ll talk about her story of change from political science, to data science, as well advice she has for those looking to enter the tech industry.

Madison’s LinkedIn profile


Transcript

Why did you move to the bay area?

The Bay area was really calling to me. I’m Vegan, you know, you kind of think of hippies or you think of Berkeley. I’ll really vibe with those people for the vegetables. I moved here partly for the vegetables, a partly for just trying to like get some exposure to California. I actually had never lived here before. Out of all the places I’ve lived, I never made it to California. I had a few friends out here from Undergrad and I thought maybe I’d have somewhat of a network. And so just kind of spontaneously I decided, hey, I’m going to move to the bay area and moved two weeks later.

So you moved to the bay area on a whim. What were your expectations of the bay area before you got here?

So believe it or not, I didn’t really know much about the tech world, so my expectations are really just laid back, people who are really passionate about the environment, who were passionate about eating healthy and I wasn’t completely wrong, but I was really blindsided by the whole tech community, the tech culture out here. And that was over four years ago in. It’s just steadily taking over. How were you blindsided? I have a liberal arts background. I got my first masters in political science and was not even like the tech world was not relevant to me.

People are in a bubble here and when you come and you’re new and you don’t fit that cookie cutter role, people don’t necessarily know what to do with you. They don’t know how to interact. You can’t talk about coding, you can’t talk about math, you know? Um, it was kind of off putting, but I was really optimistic and was happy to be out here and obviously there is a community of people that’s passionate about the environment. There’s vegans in tech, there’s all kinds of meetup groups out here and I was definitely able to meet some people that I aligned with.

So what did you find when you got here?

I found that I could not get a job without a technical background. Literally I looked for two years and was very stubborn about it. Even got a nannying job to fund myself doing the job hunt. I was, graduated from NYU on the Dean’s list with honors, went and got my master’s degree, had had great grades and I couldn’t get a job.

So why did you get into data science?

Speaker 1: So that was like a weird pivot as we do in life. Right? It’s, I like to think of life as a labyrinth. I’ve always been passionate about public health in health policy and I actually wanted to apply to some phd programs in health policy and I got to the interview stage, which for PhD’s is really good. I’m in the top five, 10 percent of candidates, but I was really lacking a quantitative background and everything nowadays seems to really demand some level of coding experience or a math experience or at least some understanding of statistics which I didn’t have at the time.

I was a nannying at the time while I was looking for jobs and applying to this phd program. And the person I was nannying for wasn’t data science and he kind of was like, oh, that sounds exactly like data science to me. You should look into that. And so within a few months of hearing data science for the very first time and getting rejected from phd programs, I actually against spontaneously applied to UC Berkeley’s master’s degree in data science. So I applied to my second masters and I found out six weeks later I was accepted and that I would be starting. I had applied for the fall. It was November or December and they said: actually you can start in January if you want. Then I was like: Okay here we go!

So we’re coming up on three years of experience in the field, both the master’s degree and about a year and a half of working experience. What would you say you’ve learned so far working in the industry as a data scientist?

I have learned a tremendous amount of things. Um, some of it is just we’re capable of so much. Um, you know, imposter syndrome can be really paralyzing and I still suffer from it every single day. I’m now a senior data scientist. I don’t know who decided to give me that title. It’s pretty exciting, but I feel every day like I should be an intern, you know, and…

…I’ve heard this: that there’s so much in data science to learn that it’s almost nearly impossible to learn it all, but you’re expected to know it all

Yes at the same time. That has kind of like two sides of it, which is I’ve learned that I am capable of learning so much beyond what I thought I could. I didn’t even know what deep learning was a year and a half ago and now I do it every day and I didn’t know how to code and now I code every day and I didn’t. I hadn’t had math since high school and now I do math every day and so we’re really capable of so much, but I also learned that it’s never ending.

So this industry demands a lot. It’s never satisfied. So even if you are learning a lot, no one is judging you on the progress you’ve made. People are judging you on how far you have to go and so that’s kinda hard to cope with. And so I’ve had to learn a lot about maintaining my sanity, going for walks, balancing with a healthy lifestyle, you know, in turn that you get to know yourself a lot better when you’re put under pressure.

What is the biggest takeaway you’ve you’ve had in the last year of working in the industry?

Tech and data science promise a lot and they do often deliver, but it still falls short. I really, really am excited about what hopefully is to come in the future with deep learning, being more accessible through things like AWS. My, I hope to see more data for good. I hope to see people actually being being helped are people really going out of their way to create startups that aren’t just about making money, whether they’re about helping people and solving societal problems. And I hope to maybe add to that. I hope that we can all come together and contribute to a better society.

So you moved out here on a couple of weeks notice on a whim. As you say, you got your second master’s degree in data science with a few weeks notice. What’s next?

Typical to my personality, I want to do about a million things. I think I might go for a third master’s degree. You know, of course just collecting degrees over here. I’m really interested in getting an MPH instead of pursuing a PhD in public health. I think I want to get a masters in it, and the reason for that is really not necessarily that I’m going to abandoned data science. I think it’s fruitful and I think it’s rich in skills and I’m not ready to leave quite yet, but I would like to be more educated on the public in what we’re suffering from and how I can better help.

There’s an untapped industry for the most part in healthcare where there are all these issues that are only starting to be touched. So we’re looking at clinical notes. We’re looking at radiology, we’re at insurance. Insurance is like 90 percent of the healthcare tech jobs these days. And not that there’s anything wrong with that, but I just would like to be better educated about how I can help others.

And if you could go and tell yourself four years ago before you moved out to The Bay, what would you tell yourself three, four years ago?

I’d say take a marathon approach, not a sprint. I have been trying to sprint and I’ve been so close to burnout so many times because no one’s going to turn you away from sprinting. A lot of people will look at you as a battery, not a human, and they will want to use you until you burn out and just remember to look out for yourself and that there’s plenty of time to learn and no matter how quickly you learn, it will never be enough and there will always be more. Especially when you’re in data science and especially when you’re in deep learning because it’s cutting edge, it’s always developing and this stuff is so much fun, but it can also be overwhelming, but if you just go slow and steady and you stick to it, you keep a positive attitude. It will work out.