David Olojede
Data Scientist @Diversity Vc
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WORK HISTORY
Data Scientist @Diversity Vc
Architected and delivered end-to-end research analytics platform analysing companies across UK investment ecosystem, from data collection through stakeholder reporting• Designed and deployed automated data pipeline to identify, track, and analyse angel investor activity and portfolio performance, reducing manual analysis time by 80%• Led portfolio analysis initiatives for external stakeholders and partners, providing data-driven insights that informed capital allocation decisions.• Drove impact measurement framework for diversity outcomes across venture capital investments, partnering with leadership to define KPIs and tracking methodologies• Managed stakeholder expectations across technical and non-technical teams, delivering regular presentations to management throughout project lifecycle• Built interactive Tableau dashboards that enabled real-time portfolio monitoring and facilitated executive decision-making
EDUCATION
University of Portsmouth
Master of Science - MS, Data Analytics
Landmark University
Bachelor of Engineering - BE, Electrical and Electronics Engineering
Cranfield University
Doctor of Philosophy - PhD, Data Analytics in Industrial Processes
ABOUT DAVID OLOJEDE
I architect ML systems that create real-world impact—from making venture capital more equitable to keeping critical infrastructure running smoothly.Over the past 4+ years, I\'ve led end-to-end data science projects across technology and infrastructure sectors. Here\'s what that looks like in practice:Making VC More InclusiveAt Extend Ventures, I built enterprise data infrastructure on GCP, deployed ML models analyzing 200K+ companies and 500K+ founders, and delivered production systems that increased investment in underrepresented founders from 11% to 14.5%. This research was cited by UK Parliament and published in State of European Tech—it\'s rare that data science work influences national policy conversations, and I\'m proud of that impact.Keeping Power Grids ReliableMy PhD research applies AI to critical infrastructure. I designed the Milton Keynes transmission network digital twin (<10% error margin) and developed predictive maintenance algorithms achieving 90% accuracy in detecting asset degradation. Published in Springer Nature, this work shows how machine learning can prevent power failures before they happen.CurrentlyAt Diversity VC, I\'m building analytics platforms that process UK companies, automating investment intelligence and reducing analysis time by 80%. Real-time dashboards now inform capital allocation decisions that used to take weeks of manual work.What drives me:I love the challenge of taking messy, complex problems and building scalable solutions that actually work in production. Whether it\'s cloud architecture, ML deployment, or stakeholder management, I focus on delivering measurable results—not just impressive-sounding models.Technical toolkit: Python, SQL, GCP, TensorFlow, Scikit-learn, Apache Airflow, Tableau, Power BI, and everything needed to take projects from concept to deployment.Always happy to connect with fellow data scientists, ML engineers, researchers, or anyone curious about applying AI to solve meaningful problems.
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