Civil Engineer to Data Scientist: How I Got a Six-Figure Job with Zero Experience

about me May 16, 2023

Landing your first data scientist role is hard. It can also be incredibly frustrating, and part of that frustration comes from what I call the chicken-and-egg dilemma of job searching.

That dilemma is this: most data science jobs require experience, but you can’t get that required experience without a job.

You need experience to get a job, but you need a job to get experience. If you are looking for your first role, you can easily feel caught between those two things, and it’s not a fun place to be.

However, I did not write this post just to talk about how frustrating this is! Instead, I want to share what you can do if you find yourself in this situation, and I want to do that by sharing the story of how I got my first data scientist job with no experience.

I’m going to share the mistakes I made, what I learned to eventually get results, and bust some myths about who can be a data scientist. I hope by the end you’ll feel encouraged and ready to continue with your job search!

Before we get into my personal experience though, if you prefer visual content, you can check out the YouTube video I created on this topic.

Now, let’s look at how I got my first data scientist job.

My Background

For my story to make sense, you first need to know where I was in my life before deciding to seek a job in data science.

It was around seven years ago in 2016. I had one more semester in my civil engineering master’s degree, and my specialty was transportation engineering.

That’s right! I wasn’t even pursuing a degree in data science or even computer science. I did have some skills that were relevant to data science, such as proficiency in Python, statistics, SQL, and Github.

I used those skills to work with professors in my department on aviation research. I cleaned up flight trajectory data and built models using that data, but that was the extent of my data experience. I had never had work experience or even an internship in data science.

You are probably wondering at this point why I decided to try to land a job in data science in the first place. Why didn’t I start off on a career in civil engineering?

Deciding to be a Data Scientist

The truth is I was inspired by the Ph.D. students I was working with in my program. They all wanted a job in tech, and being around them caused me to explore the field more.

I soon saw that the world of tech had a huge potential for innovation and impact. Technology is not just a tool. It is a way to create change in the world, and I wanted to use my skills and knowledge to be a part of that change.

For example, I was really intrigued by companies like Uber and Lyft. Their products had made people’s lives so much easier and more convenient and had revolutionized the transportation industry. Companies like SpaceX and Blue Origin also interested me because they were pushing boundaries and making space exploration more accessible.

Through interacting with these students and working with data through my research, I soon found that I loved using data to solve problems. I liked both the challenges and potential of working with data. I wanted to use data to unlock insights to transform people’s lives and to do that, I knew I needed a job in tech.

I was ready to set out on this new career path, but I quickly ran into an obstacle. After discussing my hopes with my fellow students and some professors, I learned that no one in my master’s program had ever landed a job as a data scientist before. I was very worried that my educational background was just too irrelevant, and I would never be able to land a job in tech.

Encountering Obstacles

Despite my fears, I decided to take the plunge and apply to data science jobs anyway. I only had a semester left, and I needed to find a job. I got online and started browsing data scientist job posts, and I quickly ran into two major obstacles:

  1. I didn’t have all the required skills. I hadn’t learned anything in school about things like machine learning, data pipelines, ETL, etc. I found courses online and worked to bridge this gap. There were a lot of resources, so I felt that I could acquire the needed skills.
  2. I couldn’t land interviews. No matter how much time I spent crafting my resume and writing cover letters, I just kept getting rejections. I sent out 500 applications in the first three months and didn’t get a single interview.

That second obstacle quickly became my biggest concern. If no one would give me an interview, would I still be jobless after graduation?

Finding a Mentor

My job situation didn’t look good, but then something changed. I was working with a professor on a research project, and one of his former students, who I had met in the past, came to visit. She was an Applied Scientist at Amazon.

This was a great opportunity for me, and I didn’t let it pass. I was able to talk with her about my own job search, share my concerns, and ask for some advice.

Fortunately for me, after talking with her, she eventually became my mentor. She agreed to meet with me for a 1-hour session every two weeks. With the guidance of someone experienced in the field, my next three months of job searching were very different from the first three months.

New Strategies, Better Results

The things my mentor taught me in our sessions proved invaluable. Specifically, there were three things I learned that turned my job search around.

1. Pointing Out Misconceptions

Because I had never worked in the field and had been job searching on my own, I had a lot of misconceptions about how to best land a job in data science.

For instance, from looking up information online, I had the impression that writing great cover letters was really important. I spent a ton of time on them, but my mentor revealed that this was actually a waste of time. Many recruiters don’t look at cover letters. There are too many applicants, and they just don’t have time. Knowing that saved me a ton of time on future applications.

Another misconception I had was that you shouldn’t reach out to recruiters and hiring managers. I was shy and hesitant about doing this, and I had no idea what to do if they failed to respond.

My mentor helped me learn to craft better messages and to follow up consistently to show my interest and persistence. This proved extremely important because following up with recruiters and hiring managers was how I eventually landed some interviews!

2. Reviewing My Study Plan

I knew I had some skill gaps that I needed to fill, but because I had never worked as a data scientist, it was hard to know precisely which gaps were the most important to fill. My mentor reviewed the subjects I was studying and helped me determine what was important and what was not relevant to landing a data science job.

For example, I was taking some computer science algorithm courses to enhance my software engineering skills. However, these skills actually weren’t that relevant to data science. My time was better spent studying other things.

3. Interview Prep Tips

My mentor also shared tips to prepare for interviews and what I needed to pay attention to when talking to potential employers.

She explained things like how to engage with the interviewer and how to give structured answers and demonstrate my thought process. Being prepared for interviews gave me a lot more confidence when I did finally land an interview.


As you have likely already guessed, I did finally land interviews! After submitting over 800 applications, which included raw applications, referrals, and cold emails, I got 5 interviews and 2 offers. I took one of those offers and became a data scientist for a supply chain management start-up. I had landed my first data scientist job, and I was making six figures!

What was especially amazing about the place I ended up working was that they hired me on the spot. The onsite interview was supposed to be 5 rounds, but after the first round, which was a presentation, the VP told me I was hired! After six months of job searching, it was a huge relief.

The Importance of a Mentor

Ultimately, I do not think I would have achieved what I did without my mentor’s help. She helped me review and improve the presentation that landed me that job, taught me the importance of showing up confidently to interviews, and showed me how to leverage skills I already had to impress employers.

When job searching without any guidance, I was worried about what I didn’t have, but my mentor encouraged me to instead focus on my strengths. Companies care more about what you can contribute than what your background is.

This was the mindset shift that helped me show up confidently and deliver a killer presentation that got me hired on the spot, and I never would have made this shift in my thinking without my mentor. All the practice and conversation with her were incredibly helpful, and I continue to learn from her to this day.


That’s my story, but how does it apply to you?

There are a couple of things I learned during this journey that I think can help anyone who finds themselves looking for a data science job without a lot of experience.

  1. You don’t have to do it by yourself. My mentor was incredibly valuable to me and helped me finally gain traction in my job search. Having someone to encourage you and offer insights is so important, so don’t feel like you have to go it alone! Reach out to others because sometimes a little guidance is all you need.
  2. Invest in yourself. Taking the time to work with coaches and mentors is an investment in yourself and that is something you will never regret. Getting the support you need is worth it. I work with coaches in lots of areas such as with my health and communication. I have never regretted working to improve myself!

That’s my story of how I went from a civil engineering student to a data scientist!

I hope this has encouraged you and given you the motivation not to give up on your job search. You will face obstacles, but you can do it. Don’t be afraid to ask for help! Sometimes a little guidance is all you need to start making progress.

Effortlessly learn data science and prepare for data science interviews with our free, organized resources.
Download All Resources Now!