Rachelle Perez is no stranger to the world of tech, having been part of the instruction team for a data analytics bootcamp. But as the field of data evolved to include new tools and technologies, Rachelle knew that it was only a matter of time before her current skill set became obsolete.
The bootcamp’s Data Scientist program was exactly what Rachelle needed to level up her skills. Before she even graduated from the program, she secured a job with Spotify as an Associate Data Analyst, a job that pays over $90,000 a year. Check out how Practicum by Yandex helped Rachelle future-proof her career in tech.
Tell us about your background. What were you doing before attending the program?
Before attending the program, I was a Data Analytics Resident tutoring and providing guest lecturing for a data analytics bootcamp sponsored by the city of New York to train the workforce of tomorrow. Although I loved the role right after my own data analytics bootcamp, I did not have formal experience in data analytics then.
What motivated you to explore a new career or upskill in your existing field, and why did you decide to pick this program?
Before Practicum by Yandex, I took a basic data analytics bootcamp, which was a great foundation… However, I also learned that data was much more complex, requiring continuing education to do well. Practicum by Yandex’s program was exactly what I needed as it is specifically intended to build up [knowledge in] data science concepts, which helped me build my confidence in that arena.
How did you finance your education, and what were some of your biggest considerations when making this choice?
I was very lucky to receive a scholarship for Practicum via Women Who Code.
What did you like about the program? Are there any highlights that stood out to you?
The content itself is written in an approachable, easy-to-understand way, which is unlike a lot of data science content out there that requires a certain level of knowledge in math and statistics…There were many projects that were clearly described and assessed via code reviewers who provided thorough feedback. It made me get better as a programmer in each project iteration.
[Receiving] personal feedback from code reviewers was the single most impactful part of the course in my development…I was particularly impressed with how the program handled communication via Slack.
How did you fit the program into your schedule?
The asynchronous nature of the program made it possible. I was able to work on lessons and projects at odd times…and live webinars are always recorded and immediately accessible. Thankfully, the program is set in sprints with hard/soft deadlines. Although I was learning asynchronously, I had deadlines to meet which kept me motivated.
Can you give us any examples of projects that you worked on during the program?
I created a model that took in movie reviews and labeled the sentiments as negative or positive. I challenged the model with actual Rotten Tomato reviews of the cult classic Drop Dead Gorgeous, and it was so exciting to see the results.
Do you have any advice for someone considering this program?
The program is excellent as an entry into data science and understanding the foundations of Machine Learning…The content was consciously made to be easy to understand and digestible…By the time you finish, you will have a large portfolio to show your work.
How did the program support you in finding a job?
I found a new job towards the end of the program, hence, I did not utilize the post-bootcamp Career Development portion. Although I did browse through its content and resources to improve my resume and LinkedIn.
Was the job search process different from what you expected?
Yes, but I was well prepared due to the projects. The job search included a lot of case studies and that is what a project is at its core: a problem and a data-driven solution.
How many companies did you interview at? How did you choose which one to work with?
I actually got two jobs during the program. I only applied to one company when I got my first Data Analyst job, and by the time I got my second [job], I interviewed for five to 10 companies. By then, I had a clear goal of the type of company I was looking for.
How are the skills you gained from the course useful in your current career?
I work for a company that is very data-driven. Most decisions are based on experiments, which I learned about in the course. And machine learning is part of our product, which I am now more familiar with. A side benefit was also a lot of practice in Python, which I use in my job to analyze data to address business questions.
What do you think is different about your life now versus before the program?
Before the program, I had no experience in data science and was not confident in conversations that involved data science or ML. Now…I know what ML can and can’t do, what the basic (and frankly the most common) ML models do, the value of understanding and preparing the data that goes into ML models, and how to interpret findings in a conservative way.
What do you find fulfilling about your current line of work (software engineering, UX design, etc.)?
I find a lot of comfort in being an advocate on using data to inform decisions and knowing how to do it well…In my current role, intuitions and hypotheses are tested by data professionals to ensure we are making smart decisions that improve the business.
What do you enjoy about working at your current company, are there any specific perks you enjoy (work hours, pay, time off, etc)?
Spotify is without a doubt the best employer I have ever had…Their three most important benefits for me are their Work From Home Anywhere policy, paid family leave, and fertility benefits.
Most employees (if not all) have a hybrid or completely remote work option, and you can work from any Spotify office in the world. If you have a child, you get six months of paid leave…and they provide a hefty stipend for fertility benefits if/when you need it. Besides the benefits, I love that our product helps people discover music and that the culture is caring and fun.
Do you have any job search advice for someone considering a career in your field?
It can be easy to over-focus in practicing your hard skills (SQL/Python coding), but it is even more important to practice communicating the skills you have confidently and extracting valuable insights from your data.