Data Scientist at JP Morgan Chase: Elsa Vera Amores

Posted on Jul 25, 2019

Project GitHub | LinkedIn:   Niki   Moritz   Hao-Wei   Matthew   Oren

The skills we demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

After traveling across continents for her first career as a medical researcher, Elsa Vera Amores made a career change to data science by taking the in-person immersive Data Science Bootcamp at NYC Data Science Academy. She now applies the skills she learned at the bootcamp and her passion for machine learning as a Data Scientist in the Workforce Analytics team atJP Morgan Chase Admissions Officer, Monisa Felson, met with Elsa to discuss her career journey from Molecular Biology to Data Scientist, learn why she decided to switch to data science, and how the knowledge she gained at the bootcamp helped her land a job at JP Morgan Chase.

Could you fill us in on your background before you decided to pursue data science?

I’m originally from Madrid. That’s where I earned a Ph.D. in biochemistry, molecular biology, and biomedicine at the Universidad Autónoma de Madrid. After I completed my studies there, I came to the US for my post-doc work. I was a Research Fellow at the Memorial Sloan-Kettering Cancer Center for seven years. 

Why did you decide to study data science?

For my job at Sloan-Kettering, I had to use R. I took some courses to learn it. After that, I was hooked and decided to immerse myself in a data science course of study at the bootcamp.

What challenges did you encounter in making the transition into a data science career?

The main challenge I faced was deciding to quit my job without having another job lined up and dealing with that uncertainty. I was concerned about how long it may take to find a job. Will I like it? Will I be good at it? The bootcamp itself offered an extra challenge in taking on prework courses, while I still had deadlines at work. But it helped me create a strong foundation before even we started with the bootcamp.

How did you find the bootcamp experience?

The 12 weeks of the bootcamp were probably the most productive [weeks] of my life. The high demand for the program, the classes, and the deadlines made me realize that the bar was high. That accelerated our learning process. Another thing that really worked is the immersion in the bootcamp with a heavy focus on coding and machine learning. The experience was similar to being fully submerged in the culture and language of a foreign country; that accelerates your learning process.

What was your favorite part?

I enjoyed the machine learning classes. They were very dense and very difficult to follow, but once you understand all the pieces, you get to understand the bigger picture- and that was very rewarding. I really immersed myself in machine learning and would even review the videos at home with my husband in the evening while having dinner This helped me revise the classes and get a foundation in the subject of my interest.

How did you find the projects you worked on during the bootcamp?

The time we had the projects ranged from two to three weeks, which was tight, but we made it work. I worked on four projects.

For the R Shiny App - Data Science to Analyze Big Genomic Data, I  created an interactive Shiny dashboard for Next Generation Sequencing data analysis to identify cell types based on gene expression patterns. The topic was based on the type of work I had done at my job.

The Machine Learning (Scikit-Learn)- the project was: The Strength of Linear Models in Predicting Housing Prices in Ames, Iowa. I built a machine learning pipeline to predict housing prices on a Kaggle dataset combining regularized regression and boosted tree-based models. This was based on a Kaggle data set with the addition of my insight to bring out my own story. 

The Web Scraping (Python)- Global Data Scientist Market Demand Analysis involved scrapping data from the Indeed website (Scrapy) to compare Data Scientist jobs in different countries. I prepared visualizations for that, as well.

The Capstone project (NLP)- Recommendation App & Restaurant Decision Tool for Two was done in a group. We designed a hybrid restaurant recommendation system, combining content-based and collaborative filtering approaches based on Yelp dataset. I enjoyed working on that because I enjoyed working with my team. We were very compatible and worked very hard. We learned from each other, and we helped each other. The final result was rewarding. l learned a lot about NLP and recommendation systems. That has proven very useful to me in my current job. 

How did you go about your job search, and in what way was NYC Data Science Academy helpful for you? 

My job search took about a month. During that time I worked on my technical skills while searching for the right opportunity. I applied for jobs daily and prepared for onsite interviews and checked in with regularly with the hiring team at the academy. The team at NYC Data Science Academy gave me a lot of helpful tips and referrals. I did two onsite interviews. I was accepted in both, one a couple of days after and the other a few weeks after. 

Can you tell us what you do in your job and how it relates to what you learned in the bootcamp?

The work I do is used by the company’s HR department to optimize the hiring process. I work with text analytics to analyze the text of surveys. It involves a lot of coding, machine learning, and meetings. I enjoy doing machine learning in an environment in which I’m deploying model, and my work has an immediate impact. 

I learned everything I know about machine learning from the bootcamp. The curriculum gives you all the tools you need to develop your data science skills for the job. 


About Author

Pranjali Galgali

Pranjali Galgali is a Marketing and Communications Associate, NYC Data Science Academy. She is a Master's in Digital Media and Strategic Communications from Rutgers University. She enjoys reading and writing about data science, upcoming technologies and loves interviewing...
View all posts by Pranjali Galgali >

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