Adeyemo Victor
Welcome! I'm a Data Scientist with 9+ years of applied experience across academic (900+ citations), and public/private settings (i.e., cybersecurity, sports, health, retails and marketing), with a PhD in AI-based performance modelling using large-scale multi-modal sports tracking data. Expertise in data modelling, feature engineering, extraction and selection, factor analysis, statistical analysis, survival analysis, clustering/segmentation, association rule and pattern mining, supervised (machine and deep learning) modelling, natural language processing, causal inference, explainable and responsible AI using structured, unstructured, and time-series data. Led projects aligned with National Digital Weight Management Programme, integrating multi-modal and multi-sourced data to develop KPI monitoring dashboards and advanced analytics models for population health outcomes. Experienced in Agile project management and applied research frameworks, with 900+ academic citations and a track record of impactful solutions through data science.
REVIEWS FROM CLIENTS
Led predictive analytics for NHSE’s Digital Weight Management Programme across 8 NHS-linked regional contracts.
Led an extensive...
Led predictive analytics for NHSE’s Digital Weight Management Programme across 8 NHS-linked regional contracts.
Led an extensive data audit and integrated NHS and SME health data (i.e., created metadata files and transformed raw datasets) into a unified, multi-source and multi-modal big data system for clinical insights and app strategy.
Designed and conducted user needs analysis (via mixed methods using survey forms, 1-on-1 interviews and focus group for data collection) to evaluate clients’ motivations, challenges, barriers, and satisfaction levels, leading to the standardisation of periodic customer feedback assessments.
Conducted market research on commercial applications to identify trends, assessed features and services offered, and conducted SWOT analysis.
Delivered academic literature review to identify state-of-the art algorithms for health-related analytics.
Engineered features, handled data quality issues, implemented data pipelines and modelling workflows to support statistical, predictive analytics and AI-based decision-making for health interventions.
Developed a risk assessment and mitigation strategy to address data dependencies, improving data reliability across branches.
Designed end-to-end Machine Learning pipelines for rigorous experimentations and deployed models using Azure ML.
Implemented data fairness and explainable AI methods within the analytics workflows.
Developed and delivered interactive Power BI dashboards and KPI visualisations for senior managers.
Used Agile (Scrum) methods to manage deliverables, user research, and iterative deployment cycles.
Delivered model recommendations informing digital app variable design for patient engagement tracking and health intervention outcome improvement.
Collaborated with clinicians and academics to produce peer-reviewed papers for journal and conference submissions and presented internationally.
· Built ML models for position classification, workload prediction, and performance analysis using GPS data.
· Worked with Mongo...
· Built ML models for position classification, workload prediction, and performance analysis using GPS data.
· Worked with MongoDB, Python, and R to manage and preprocess time-series and spatial performance data.
· Improved player classification accuracy by 30% through AI-driven segmentation and clustering models.
· Published peer-reviewed sports data science research with over 800 citations.
· Supported performance strategy and insights for coaches and technical staff.