ΑΙhub.org
 

DataLike: Interview with Motunrayo Kilanko

Motunrayo Kilanko is a seasoned data management and analytics specialist who has worked in the fields of data analysis, data management, and data annotation for machine learning. She works presently as a management analyst with a government healthcare agency in the State of Delaware, United States. She is also an AI enthusiast that teaches women how to use AI for their work and business. Her career interests spans Data, AI, public health, and empowerment of women.

She is the founder of Femote, a social impact startup that provides business support and outsourcing services such as data annotation, data processing, and data entry to companies around the world by trained and skilled female professionals from Africa. She also created the Femote School, which aims to close the gender gap and promote women’s participation and inclusion in the digital economy by providing them with digital skills training and upskilling. Her start-up, Femote, was featured in MovingWorlds. She has also been featured in Upwork’s 2022 Investment Impact Report and Stack Journal.

Hi Motunrayo, could you recap what were your first steps in the field of data, and how you got started?

My data career journey began during my undergraduate years as a public health student at Babcock University. Towards the end of my third year at the university, an assistant professor in my department, some of my classmates, and I participated in a research and statistics training program.

It was an immersion program for me where I learned about research methodology, some statistical methods, and data analysis. The session I enjoyed the most was the data analysis session using tools like SPSS and Epi Info. After the training program, I helped my classmates analyze their final year papers and also trained some students on how to analyze their data using SPSS.

This was the beginning of my data journey, and I continued to work with data in various capacities throughout my postgraduate studies and early career. In 2018, I transitioned to tech, and in 2019, I started my journey as a data annotation specialist and data analyst at a startup in Ibadan, Nigeria.

Can you share a particularly challenging moment in your career and how you overcame it?

One of the challenges I faced in my career was moving from the learning phase to the point where I started applying for jobs. I also thought I needed to know all the tools before I could start using my skills.

I later discovered that most of the learning you do is on the job, especially in the beginning. Sometimes you learn about one tool and another tool is used by your prospective employer.

I have learned to keep my learning up to date and get my hands dirty quickly when I need to learn something new or a new tool or platform is needed for my work.

What advice would you give to someone just starting in data?

Don’t wait till you are an expert before taking on projects. Don’t stay too long in the learning phase, explore internships, apprenticeships, and free work to start.

Also, go on LinkedIn, search for the kind of role you want, and make a list of the most sought-after skills; both hard and soft skills and learn those quickly. AI is disrupting every industry, so start learning and exploring AI tools to do your projects.

AI will help you 10x your productivity as a data professional but be careful not to use sensitive data on public AI tools.

You can keep up with Motunrayo on LinkedIn and Mainstack.




Ndane Ndazhaga is a Data Scientist who loves using data to improve businesses and help make decisions.
Ndane Ndazhaga is a Data Scientist who loves using data to improve businesses and help make decisions.

Isabella Bicalho-Frazeto is an all-things machine learning person who advocates for democratizing machine learning.
Isabella Bicalho-Frazeto is an all-things machine learning person who advocates for democratizing machine learning.

Datalike

            AUAI is supported by:



Subscribe to AIhub newsletter on substack



Related posts :

Interview with Yash Saxena: how is external knowledge used in AI systems?

  15 Sep 2026
What happens to information from external sources as it moves through an AI system?

When AI art has no author: Study finds generated images often can’t be traced to training data

  14 Sep 2026
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.

AI in nature conservation: powerful tool or dangerous shortcut?

  11 Sep 2026
AI provides opportunity for future biodiversity conservation but introduces risks .

Improving the process for large-scale recommender systems: an interview with Haruka Kiyohara

  10 Sep 2026
Credit-assigned policy gradient for early stage retrieval in two-stage ranking.

The Machine Ethics podcast: Data Collective with E.M. Lewis-Jong

Ben chats to E.M. Lewis-Jong about the promise of AI and making human connection easier, speech recognition and supporting linguistic diversity, making useful technologies that have a purpose, and more.

AI for ethology: an interview with Isla Duporge

  08 Sep 2026
Deep learning is becoming a powerful tool for understanding animal behaviour and tracking populations.

AI in cardiology: The path to practical application carries risks

  07 Sep 2026
Can artificial intelligence help us better combat cardiovascular diseases? Legal researcher Hannah van Kolfschooten urges caution, as there are still many legal issues that need to be resolved.

AI-powered camera system offers low-cost way to monitor bumblebees

  04 Sep 2026
Researchers have developed a semi-automated method that uses remote cameras to survey bumblebees and potentially other insects.



AUAI is supported by:







Subscribe to AIhub newsletter on substack




 















©2026.05 - Association for the Understanding of Artificial Intelligence