Hey guys. Welcome back to my channel. Today, I want to tell
you a story about myself: how I went from being a civil
engineering master's student to becoming a six figure data scientist. It was an incredible six month journey and I learned a lot along the way. The reason I choose this story
to share with you today is because the current job
market is really tough for people who are looking for their first data scientist job and I wish I could
offer some encouragement by sharing my own story with you.
I know one common frustration is that companies want
experienced data scientists and most job posts require
years of experience. But if you are looking for your
very first data scientist job you will face the chicken and egg problem. You cannot get hired
without experience and you cannot get experience
if you cannot get hired. This is definitely a vicious cycle so I want to share with
you how I overcame that. I believe that with the right
direction and hard work anyone can achieve their dreams. I know this because I've done it myself. So let me share with
you how I got started. I began my data science
journey about seven years ago in 2016 when I had only one semester left for my civil engineering master's degree.
My specialization was in
transportation engineering. I had some relevant skills. For instance, I was proficient in Python, statistics, SQL, and GitHub. These skills were honed during my research in the transportation
engineering department. I worked with a couple professors in the Department of Aviation Research and got opportunities to
clean up a large amount of flight trajectory data and
build models using the data. However, I never had any internships or work experience in data science. Now, you might be thinking,
"Why is someone without a degree in data science nor any work
experience or internship in the field interested in
finding a job in data science?" Well, I was inspired by the PhD
students I was doing research with, who all wanted a job in tech. As I explored the world of tech I began to see its potential
for innovation and impact. I realized that technology
is not just a tool but a way to create change in the world.
I felt compelled to be
a part of that change to use my skills and
knowledge to make a difference. For example, I was really interested in the products that Uber
and Lyft have developed. They have made people's
lives so much easier and more convenient by revolutionizing the
transportation industry. I also admire how companies like SpaceX and Blue Origin are
pushing the boundaries of space exploration and making it more accessible
for future generations. I loved the idea of creating something new and using data to solve
problems that had never been solved before. I was driven by the challenge of working with data and the potential of using it to unlock insights that could
transform people's lives and for me to be a part of the change I need to find a job in tech.
But what was particularly
discouraging was that none of my fellow master students
were able to get a job as a data scientist. After speaking to my peers and two professors in
the department, I learned that no one from the
same master's program had landed a data science job before, so I was worried about my
irrelevant educational background and how I could land a job in a completely different industry. I decided to get started
anyway because I only had one semester left and
I needed to find a job. After I made the decision
to find a data scientist job I started browsing job posts.
I quickly faced two major obstacles. First, I didn't have all
the required skills, such as machine learning, data
pipelines, ETL, et cetera. I had never taken any machine
learning courses in school so I searched for some courses online and tried to build my skill set by consuming as many
online courses as I could. It seemed that there were plenty of resources out there that
could help me bridge the gap. At the end of the day you can always acquire
more skills if you want right? However, these
skills were not enough. The second obstacle was
completely out of my control and it seemed impossible to overcome. It was pretty hard for
me to land interviews.
I spent a lot of time crafting my resume, asked more than 10 people to
review it and give me feedback, and spent numerous hours
writing cover letters for different positions. After sending out 500 applications
in the first three months all I got were rejections. I did not get any
interviews from any company. I was worried that I was going to spend
a few months job searching and still be jobless after graduation. As I was working with a
professor on a research project, his former PhD student and a current alum whom I also
knew and had met in the past came in to visit and say hi to everyone.
She was an Applied Scientist at Amazon. It was very fortunate for me because I had the
opportunity to talk with her, share my concerns, and
ask for some advice. I told her that I had been job searching for quite a few months and felt stuck. Actually, I was feeling like giving up because it was really hard
for me to land interviews and without an interview,
how could I land a job? Also, I didn't have
all the skills required for a data scientist job. After speaking with her, she
eventually became my mentor for which I am very thankful. It was a momentous help
for me to land a job and become a data scientist. I am still thankful to
this day that I was able to get guidance from a more
experienced professional and without it, I'm not sure
how long it would have taken or where I would be today. So she agreed to meet
with me every two weeks for a one hour mentorship
session, and those sessions were invaluable. Specifically, there were
three things she helped me with that have been game changers. First, she pointed out
many of the misconceptions I had cultivated by consuming
information online, such as the belief that writing
great cover letters is important.
So I used to spend a lot of time writing them along
with my job applications. She told me it was a
huge waste of my time as many recruiters don't even look at the cover letter due to the
high volume of applications. This single piece of advice
saved me a ton of time. Also, I used to be very shy about reaching out to recruiters
and hiring managers. I did not know what to say,
and if they didn't respond I didn't know what to do. She helped me review my
messages, and she told me that it's not realistic to
expect everyone to reply to me immediately. It's important to follow up with recruiters and hiring
managers consistently to show my interest and persistence. You know what? That's actually how I eventually got some interview opportunities. Secondly, she reviewed my study plan so that I could focus on
what was most important and not waste time studying
anything irrelevant to landing a data scientist job. For example, I was taking
some computer science algorithm courses to enhance
my software engineering skills and she told me that it
was not super relevant to data science, so I knew
where to spend my time on things that are most relevant.
