May 13, 2024

Data Scientist Spotlight: Uchenna Johnpaul

1. Please introduce yourself, what you do, and your current role.

My name is Uchenna Johnpaul Aniekwensi. Originally from Nigeria, I now reside and work in Germany. I work as a lecturer and researcher at the University of Applied Science Offenburg (Hochschule Offenburg). My primary role involves teaching data science to students and assisting them with projects and theses. Additionally, I contribute to energy-related projects, focusing on power quality and electromobility. 

2. Tell us about your path into data science. What inspired you to pursue a career in the field? Is this what you’ve always wanted to do?

After completing my bachelor’s degree in chemical engineering at the Federal University of Technology Owerri, Nigeria, I joined WEAFRI Well Services Company Limited in Warri, Nigeria, as a trainee and later a field engineer for five years, focusing on coiled tubing and cementing. I developed a keen interest in the data department due to its vital role in supporting these areas. Despite challenges, like offshore work and limited internet access hindering my master’s program, I decided to pursue further education. Unable to balance work and study, I took a tough call to leave work without pay to pursue a master’s in Renewable Energy and Data Engineering, aligning with my passion for energy and data science. After graduation, I continued working at the university.

3. What are some of the biggest challenges you have faced on your career journey, and how have you overcome them?

One of the challenges I’ve faced in my career was in my recent project,  the lack of sufficient data. To address this, I augmented and cleaned the data produced by the application to enhance its quality. Additionally, issues such as scalability and model integration posed challenges, as each model built had its own unique circumstances. Collaborating with the DevOps team helped resolve many of these challenges. Lastly, communicating findings to cross-functional teams presented difficulties, but I overcame this by employing visualization techniques to make my presentations clear and concise.

4. What advice would you give to those who are just starting in AI and data science? What skills or qualities do you think are most important for success in this field? 

a. Stay curious and never stop learning.

b. Engage with others in the field.

c. Focus and specialize on your specific interests in the field.

d. Be resilient in the face of challenges.


i. Problem-solving skills and mentality.

ii. Communicating your findings in simple and clear terms.

iii. Patience to experiment with different approaches.

iv. Creativity and innovation in the face of new projects.