Bridging the gap: Oladipo Osonuga on machine learning, challenges, future of data science

Taking a machine learning model from prototype to full-scale deployment is one of the biggest hurdles in artificial intelligence. Oladipo Osonuga, a data scientist and machine learning engineer with two years of experience, has navigated these challenges firsthand.

In this interview, he shares his journey, the lessons he has learned, and his vision for using data science to solve real-world problems. He also addresses the evolving digital space, the importance of inclusion in tech, and the need for continuous learning in an ever-changing field.

It’s the belief of many people that digital space is meant for men how would you react to this claim?

I believe this is a misconception rooted in outdated societal norms. Like any other field, the digital space is meant for everyone, regardless of gender. Women are making groundbreaking technological contributions, from data science to software development. The narrative shifts as more women join and thrive in the digital space. Currently, most digital companies are now leveraging creating an inclusive environment where everyone can explore their potential without bias.

How would you describe yourself?

Machine learning engineer with two years of experience, I specialise in developing, deploying, and optimising machine learning models.

I am a machine learning engineer with two years of experience, I specialise in developing, deploying, and optimising machine learning models. Skilled in Python, TensorFlow, PyTorch, and Scikit-learn, experience working on projects involving data preprocessing, feature engineering, and model selection for tasks like classification, regression, and clustering. I am proficient in using cloud platforms such as AWS and Azure for model deployment, I also have a strong understanding of algorithms, data structures, and software engineering principles.

Lastly, I am passionate about leveraging data to solve real-world problems and continuously improving model performance through experimentation and fine-tuning.

We have read about your exploit within the field of data science and machine learning, how did you start your career?

My journey began with a curiosity about how data can reveal patterns and solve problems. Along the way, I took online courses, enrolled for a second master’s degree in data science, worked on personal projects, and joined communities that helped me grow. It wasn’t a straight path, but every step taught me something valuable. Additionally, my first job as a data scientist at CDcare Nigeria enhanced my confidence in the field.

What are the challenges navigating through the ups and down of your new found field?

One challenge is staying updated in such a fast-paced and ever-evolving field. The tools and techniques are constantly changing, so continuous learning is crucial. Lastly, breaking into this field in Nigeria can come with limited access to mentorship and resources, but perseverance and networking have helped me overcome those obstacles and it has opened valuable opportunities. Additionally, I volunteer as a mentor to help others overcome similar obstacles. I believe in being part of the solution by sharing knowledge, offering guidance, and creating pathways for more men and women to thrive in tech. It’s rewarding to see others succeed while breaking barriers together.

⁠Since when have you known that you are going to be a data scientist?

I wouldn’t say I knew I would become a data scientist, but I have always loved solving problems and working with numbers since childhood. It became clearer when I discovered how data science could combine my analytical skills with my passion for technology. I became curious and started learning more about data and its applications in solving problems.