Available to hire
I’m Ayodele Ayotomiwa Victor, a Data Scientist and Machine Learning expert based in Nigeria. I have strong experience in data analysis, statistical modeling, and predictive analytics, with a focus on applying ML techniques to biological, health, and research datasets.
I enjoy turning complex data into actionable insights to support data-driven decision making. I’m comfortable collaborating across teams, mentoring others, and communicating methods and results clearly, and I’m pursuing advanced studies in cell biology and genetics to bridge life sciences with data science.
Experience Level
Expert
Expert
Expert
Expert
Expert
Intermediate
Intermediate
Intermediate
Intermediate
Intermediate
Language
English
Advanced
Work Experience
Data Scientist at Odele
March 1, 2024 - November 29, 2025Education
MSc in Cell Biology and Genetics at Ekiti State University, Ado-Ekiti, Nigeria
January 1, 2022 - January 1, 2026BSc in Zoology and Environmental Biology at Ekiti State University, Ado-Ekiti, Nigeria
January 1, 2015 - January 1, 2021Qualifications
Infoworld Global Systems
January 1, 2023 - May 5, 2026The Data Science Micro Degree – Udemy
January 1, 2022 - May 5, 2026Data Science – SoloLearn
January 1, 2020 - May 5, 2026SQL – SoloLearn
January 1, 2020 - May 5, 2026Python Core – SoloLearn
January 1, 2020 - May 5, 2026Industry Experience
Healthcare, Life Sciences, Education, Professional Services, Software & Internet
- Developed a machine learning-based binary classification system to predict diabetes
using clinical and demographic data from the Pima Indians Diabetes Dataset.
− Performed medically informed data preprocessing, including treatment of implausible
zero values as missing data and log transformation of highly skewed variables.
− Conducted comprehensive EDA to assess feature distributions, class separability, and
multicollinearity.
− Trained and evaluated multiple models with emphasis on minimizing false negatives and
maximizing recall for early screening suitability.
− Turned an XGBoost classifier that achieved 80% recall for the diabetic class and the
lowest false-negative rate.
− Documented methodology, results, and ethical considerations, and published
reproducible code and analysis on GitHub
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