Career Nav #33: Navigating a Heavily White-Dominated Field as a Latina in STEM

Career Nav #33: Navigating a Heavily White-Dominated Field as a Latina in STEM

Written by Monica Puerto


Women Who Code Career Nav 33     |     Spotify iTunesGoogleYouTube
Faith Pueneh, Front End DeveloEngineer per at CAD Consulting Limited and Lead at the Women Who Code front-end track, interviews Monica Puerto, Analytics Associate Manager at Essential Federal Services. They discuss Monica’s path to tech, who inspires her to persevere, and how Universities can contribute to more diverse work forces.

Can you tell us a little bit about yourself and your career? What drew you to become a data scientist? 

I stumbled into it. I started my major in journalism. I wrote for my school newspaper and liked it, but my dad encouraged me to go into business. I still write, technically. I do a lot of technical writing. I started doing finance, but I didn't love the classes. My guidance counselor suggested marketing, and there was no Women Who Code back then. I think I would've loved someone to tell me, "Hey, you love statistics. Maybe you should do computer science or information systems." There's market research, business development and marketing analytics, and I enjoyed that. I started falling into data science because my excel kept crashing. I was dealing with a lot of data. I started learning Python. Python can handle a lot more data and number crunching. I started learning how to code, and I took a bootcamp. That introduced me to data science. 

Why and how did you decide to pursue your masters? 

It took me a while. It was interesting going back to school after ten years of work experience. I took a BootCamp, but it didn't show me everything. Boot camp's supposed to be short, six to eight weeks, and it's a perfect introduction. Data science is so broad and also very complex. It is based on math and stats, which I liked but wasn't my strong suit. I wanted to understand a little bit more about the intricacies of the algorithms. What pushed me was Weapons of Math Destruction by Cathy O'Neil. It talks about how algorithms are everywhere. She was a financial consultant and spoke about how the models made that stock market crash in 2011. Since then, we have relied on models, and there's this whole sector of explainability in algorithms. I wanted to understand how the black box works because it's essential when these algorithms are making decisions for us now, like cars who are driving by themselves and who gets a loan or not, or who can get out of prison. There's a lot of models that touch every part of our lives. I do see an equity gap. Like climate change, some people are paying the price that are not the ones that are making these algorithms. 

What has been your experience in tech? 

It started off well, and I didn't realize I was in tech. I was doing data analytics, but now I understand that tech is more than just software engineers with hoodies and heads down. The people participating are much more diverse. My first boss was a Latina woman. I was thankful for that. I learned a lot of lessons from her. One was, "Make sure that the company's investing in you. Don't burn yourself out for someone who doesn't have a stake in you." I feel like that's why I see a lot of high turnover in tech. People leave for better paying jobs, growth, recognition and work-life balance. I have witnessed data boys clubs where I was the only woman and felt trim. No one cared to ask my opinion and my contribution. I left that place. I was happy to see that there are areas in tech that don't look like that. My next job was more diverse, and I was more welcomed and appreciated.

Have you found other Latin women having the same experience like you? 

I have seen women being dismissed or not taken seriously. They say something, and then it's not acted on until a man says it. I also have seen the opposite. I have seen more allies, especially in my position. I see the people who advocate for us to sit at the table and care about our ideas. Only 2% of Latinas are in data science or computing. That makes me sad. I had seen many more people of color in tech than when I started ten years ago. That makes me extremely happy. Black Girls Who Code, Girls Who Code and Women Who Code all make a difference.

What are some of the hardships you have overcome on this journey? 

One of the hardest things I learned was that the patriarchy hurts us all. I always try to be inclusive and work with others no matter what level. We don't get better for hoarding knowledge or skill sets from each other.

What has your leadership experience been like for you? 

It has been good and evil. It's getting better, though. I see a lot of buzzwords with DNI. They say things like, "Oh. We care about diversity, equity, and inclusion." In my company, it's tied to our bonus, which is good. At my company, in 2021, they promoted 24% of women. This year they increased it to 37%. There are changes. I see companies publishing the makeup of their teams as much as they can..

What gives you the drive and strength to persevere? 

I see that there is change. I wish it were faster. We're on iPhone 14 and putting billionaires into space, but I see political differences. With the last midterm, more women are in Congress than ever. Women like Stacey Abrams give me hope. In tech, Dr. Lee from Stanford has pioneered computer vision. The CEOs of Women Who Code, Girls Who Code, Black Girls Who Code, the Algorithmic Justice League, who are spearheaded by women as well, teachers at my grad school, the female leadership at my company, my Latina friends who I grew up with, my mom, my sister and other strong women have propelled me to have confidence early on in my career.

How do you think tech can do better with DEI and representation? What do you think they're going to do differently? 

Take it seriously by measuring it and making it as important as a revenue goal. A McKinsey study cited a positive correlation that the more diverse the company, the more profitable they are. Regardless of profit, it's just the right thing to do. Make it a key performance indicator. Outside of the tech space, I think universities also contribute significantly. They might not think of it as much because STEM occupations are available and increasing. A study that the Congress Joint Economic Committee conducted found insufficient students to meet that demand. I haven't done this study, so take this with a grain of salt, but I feel that university teachers are not as diverse. I think it does make a difference to students if the person teaching looks like them. I think it's because you need a PhD to teach. That's a big barrier of entry. There's benefit from younger teachers too. I am more exposed to the latest technologies. Some teachers keep up to date with technology, but as you know, technology changes so fast. When I was in class, they weren't teaching the latest and most remarkable technologies. They weren't teaching cloud computing as much or using the latest open source packages. They were focusing on the methodologies which are essential as well. Universities can contribute to making the labor force as diverse as possible because they mold that labor force.

Do you have any advice for Latinas pursuing STEM and data science? 

Create a network. Communities are so important. I started teaching at meetups conducted by these communities. I felt more comfortable in rooms where people looked more like me or there were more women. That gave me the confidence to go into the more white dominated spaces because I already had some practice. There are allies in white overlooked areas. You have to find them. Look at who cheers you on, and those are the people that you should latch onto. Share your wins online. Many of us like to be humble, we don't want to brag about ourselves, but you should. You should always give back. It's important to see representation.

More Information

Guest: Monica Puerto, Analytics Associate Manager, Accenture Federal Services 

Host: Faith Pueneh, Frontend Engineer/Technical Writer at CAD Consulting Limited