By Nightengale Ben-Onyeukwu
In this exclusive interview, we have the pleasure of delving into the fascinating world of data science and machine learning with Román Josue de las Heras Torres, a seasoned Senior Data Scientist. His journey from being an educator in mathematics and science to becoming a pioneer in the field of data science illustrates the impact of combining different areas of knowledge and a strong commitment to lifelong learning.
Can you tell us about yourself?
Certainly, I'm Román Josue de las Heras Torres, a Senior Data Scientist and passionate advocate for the world of data science and machine learning. My educational background in Systems Engineering and Mathematics naturally guided me into the realm of Data Science. Leveraging this unique blend of expertise, I've been involved in developing cutting-edge machine learning models, addressing forecasting challenges through time series analysis, and harnessing the power of NLP solutions.
Can you describe your journey from being a mathematics and science teacher to becoming a Senior Data Scientist? How did your teaching experience influence your career in data science?
My journey from the classroom to the world of data science has been incredibly rewarding. My years as a math and science teacher at a bilingual school in Honduras instilled in me the art of conveying complex concepts in accessible ways to children and teenagers. This experience of simplifying and communicating proved to be a cornerstone in my transition to becoming a Senior Data Scientist. It equipped me with the skill to bridge the gap between intricate data insights and a diverse range of stakeholders.
During my teaching tenure, I honed my ability to break down challenging topics, making them understandable to students with varying levels of expertise. This skillset seamlessly transitioned to the world of data science, enabling me to effectively communicate complex findings to technical teams, non-technical decision-makers, and clients. It's this unique blend of pedagogical expertise and data science acumen that continues to drive my passion for teaching and creating meaningful data-driven solutions.
What motivated you to pursue a combination of Systems Engineering and Mathematics in your education, and how has this interdisciplinary background benefited your work in data science?
The drive to connect theory with practical application led me to pursue Systems Engineering and Mathematics. This interdisciplinary foundation provides me with a unique lens through which to approach data science challenges. It enriches my problem-solving capabilities, allowing me to synthesize diverse insights and develop holistic solutions.
While taking my last class from the Mathematics area for the Systems Engineering career, I decided to pursue a degree in Mathematics as a simultaneous career as I could not see myself without Math classes at the University. It has been one of the best decisions in my life, and at the end I finished the Math career before Systems Engineering.
Could you share a specific project where you applied machine learning to solve a challenging problem? What were the key takeaways from that project?
Certainly. During my time at Sinch, I had the opportunity to fine-tune the main model for a QA search engine named "AskFrank." This engine powered multiple chatbots, showcasing the potential of AI-driven interactions even before the era of ChatGPT. This project underscored the significance of carefully calibrating models to address the nuances of natural language understanding. It highlighted the need for rigorous testing and refining of models to ensure accuracy and effectiveness in delivering valuable responses. The key takeaway was the critical role of continually improving models to create meaningful and valuable interactions with users.
Data visualization is a crucial aspect of data science. Can you discuss your approach to creating effective data visualizations that convey complex insights to both technical and non-technical stakeholders?
Absolutely, my approach to data visualization is rooted in storytelling techniques. I craft visuals that guide viewers through the data's narrative, ensuring that technical detail and approachability are carefully balanced. This strategy, informed by my experience at Goodwall and Sinch, ensures that the insights resonate with both technical experts and non-technical decision-makers.
In the field of artificial intelligence, what recent advancements or trends are you particularly excited about, and how do you see them shaping the future of data science?
The recent advancements in Explainable AI (XAI) and ethical AI practices hold immense promise. These trends align closely with my experience at Sinch, where I specialized in NLP and deep learning. As AI becomes more integral to our lives, these advancements are pivotal for fostering transparency, accountability, and responsible AI development.
During your time as a laboratory instructor at the mathematics computer center, what valuable lessons did you learn about teaching and mentoring students in the field of mathematics and computer science?
Being a laboratory instructor allowed me to appreciate the diverse learning styles and paces of students. Patience, clear explanations, and fostering a growth mindset are pivotal when guiding students in the intricate realms of mathematics and computer science. There was a group of students in particular that inspired me. They were not from a technical or mathematical area, since they belonged to the career of social work. Nevertheless, they showed a genuine interest in the class of Statistics and the lab course of the statistical software SPSS from IBM. Their reports were of high quality and they were able to apply what they learned from the class and lab on their graduate thesis. This allowed me to see that the ultimate goal as a Math teacher or lab instructor is to empower students to apply it in their daily life, at their jobs, or personal projects. For them, we must be the bridges between theory and practice.
Could you provide an example of a situation where you had to use your problem-solving skills to overcome a significant technical challenge in a data science project?
Certainly. I once encountered a bottleneck in data preprocessing that threatened to impede an entire project. The dataset was so big that fitting a model on a single machine was nearly impossible. I applied matrix compression techniques which I learned at my Mathematics degree to reduce the size of the dataset by more than 95%, enabling the training of the model on my own laptop. Leveraging distributed computing and optimizing algorithms allowed me to overcome this challenge. It was a reminder of the importance of creative problem-solving and the potential of innovative thinking. Nevertheless, applying a relatively old but established technique also made me realize that solutions that work do not need to be novel in order to have a positive impact in a project.
How do you stay up-to-date with the rapidly evolving tools and techniques in data science and machine learning? Can you recommend any resources or strategies for continuous learning in this field?
Staying current in the dynamic landscape of data science is paramount. I achieve this through a combination of strategic resources. Engaging with online courses, blogs, and active communities like Kaggle, LinkedIn and GitHub keeps me informed about the latest trends and practices. Notably, I've also contributed my expertise through a course on Ensemble Methods in Python at DataCamp. Additionally, DataCamp serves as a valuable platform for both sharing and enhancing my own knowledge. The collaborative nature of open-source projects and engagement with fellow data scientists further fuels my growth and adaptation in this ever-evolving field.
What role does ethical consideration play in your work as a data scientist, especially when dealing with sensitive data or developing AI applications? Can you share an experience related to this?
Ethical considerations are paramount in data science, especially when dealing with sensitive information or developing AI systems. I remember a project where we were handling medical data. Adhering to stringent data protection regulations and ensuring patient privacy were non-negotiable priorities. This experience reinforced the importance of ethical responsibility in all stages of a project.
Finally, what advice would you give to aspiring data scientists who are looking to build a career similar to yours, combining mathematics, engineering, and data science expertise?
For all the bright minds who are embarking on the thrilling journey of data science, here's a compass to guide you on a path brimming with possibilities. First and foremost, lay down a robust foundation in both mathematics and programming – consider them as the sturdy pillars upon which you'll craft your data-driven dreams. With these skills in your toolkit, you're empowered to forge new trails in the world of data science.
But remember, the road ahead isn't just about accomplishments; it's also about embracing the inevitable bumps and hurdles. Welcome failures as your allies in growth. Every stumble is a stepping stone toward a higher vantage point of understanding. Be fiercely curious, for it's the relentless curiosity that fuels the most remarkable breakthroughs. In a landscape where knowledge evolves at warp speed, your thirst for learning will set you apart.
And here's where the real magic happens – within the spirit of collaboration. Engage with open-source projects, dance with diverse minds, and spark ideas through collaboration. The world of data science thrives on collective brilliance. Your ideas, your insights, and your fresh perspective are essential ingredients in the recipe of progress.
So, to the aspiring data scientists of our youth today: Envision the future, embrace challenges, and embolden your journey. The data-driven universe awaits your creative touch.
‘We
value your input and look forward to hearing your perspectives on data science,
machine learning, and the exciting possibilities they hold for the future. Feel
free to share your own experiences or ask any questions you may have in the
comments section below. Let’s continue the conversation and explore the
limitless potential of the data-driven universe together.’
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