Overcoming Self-doubt: How I Got a Job Offer as Lead ML Engineer at GCash

Heide Mae Balcera from the Philippines completed several Omdena challenges ranging from improving driving voice assistance to preventing food waste and got hired at GCash. Through Omdena, she has been able to test herself, improve her soft and technical skills, and work on her self-doubt.

Heide, what is your background?

I’m a biological sciences graduate from the University of Santo Tomas, Manila, Philippines. I have skills in qualitative and quantitative research, technical writing, and data analysis. Additionally, I spearheaded multiple projects involving research, policy and procedure development, business innovations, and community development. Outside work, I spend time exploring creative fields, volunteering and continuously learning.

What job did you get?

I work as a machine learning engineer in GCash. We develop tools for data scientists, pipelines, automation and deploy models to production. Our work enables and empowers data scientists, analysts, and even stakeholders to design data-driven decisions and strategies.

How did the Omdena experience help?

 I joined Omdena more than one year ago. Omdena challenges have improved my technical skills and adaptability to different work cultures. The challenges gave me an opportunity to learn new skills and improve my expertise. 

“Regardless of your tenure, there is always something new to learn in Omdena and from your fellows. This collaboration taught me how to manage projects, interact with others, and maintain a healthy work environment.”

Some of the projects Heide took part in:

• Using Machine Learning for Root Cause Analysis of Anomalies
• Developing an AI Algorithm for Sleep Pose Estimation to Improve Sleep Apnea Condition
• Preventing Food Waste by Building a Forecasting Model to Predict Sales of Fresh Food
• Improving Driver Voice Assistance Using Machine Learning to Reduce Road Accidents

Over 1.3 million people die each year in traffic accidents and many more get seriously injured. Over 90% of these accidents happen in developing countries, where access to modern driver support technology is less accessible.

Heide Mae Balcera is also an Omdena Local Chapter Lead in Laguna, Philippines. She’s running her first project, Mapping and Recommending Allocation of Fisheries to Increase Aquatic Production in the Philippines. 

How did it change your worldview?

Prior to joining Omdena, I was a believer in data for good projects and initiatives. I ensured that whatever project I started would be for a positive cause and would have a positive impact on all industries involved. Omdena helped reinforce this by providing individuals with opportunities to work on data science projects for a reason. After every challenge, I could see our impact and the appreciation from the clients.

What was the biggest obstacle you overcame?

As a career shifter, my biggest challenge was my self-doubt. Do I have enough experience and knowledge to be able to work in the data science industry? This is where Omdena came in. Participating in multiple projects allowed me to test myself, see where I could improve, see my strengths and abilities, and ease my self-doubt. There is no better cure for self-doubt than to do the work and prove yourself.

Is there anything else you would like to add to share your current life? 

I’d like to emphasize how working for GCash and volunteering with Omdena has given me the honor of making an impact on people’s lives. In GCash, as I build pipelines, tools, and models, I can see the value of data science in a business sense and also how it affects the everyday lives of Filipinos. Seeing our impact through financial inclusion for the everyday Filipino gives me pride as a data scientist.

On the other hand, with Omdena, I’m able to help future data scientists and machine learning engineers upskill themselves. Fulfilling to be the guide and inspiration for others, I was in their position a few years ago. On top of that, we’re able to bring data science solutions to local communities and problems —in the sense of democratizing AI.