Oluwadamilare Oyediran
I build the systems behind useful data.
I'm Oluwadamilare Oyediran, a Data Systems Engineer and Data Scientist with a Computer Engineering background, focused on building the systems behind useful data. I work across the whole path data takes — capture, validation, storage, analytics, and the interfaces people actually use — with a core stack built around Python, PostgreSQL, FastAPI, Next.js, TypeScript, Supabase, Docker, and cloud deployment workflows. My work spans research data systems, LIMS platforms, dashboards, ML/NLP pipelines, and operational tools that help teams move from scattered records to reliable decisions.
I’m based in Lagos, Nigeria, and most of my work sits where research data meets analytics — building the often-unglamorous infrastructure that makes good decisions possible.
What I build
Most of my work falls into a few connected areas:
- Research data systems
- LIMS workflows
- Dashboards
- ML/NLP pipelines
- Operational tools
- AI-ready data foundations
Current direction
Right now, much of my focus is on research data systems for agricultural science — including work connected to IITA — where data has to stay trustworthy from field and lab capture all the way through to analysis.
That pulls together a few threads: laboratory digitization and LIMS workflows, FAIR data practices, and the data infrastructure that analytics and AI ultimately depend on. The goal is always the same — move teams from scattered records to reliable, reusable data.
How I work
- Understand the workflow first
- I map how people actually work before modelling anything, so the system supports the work instead of fighting it.
- Design the data model carefully
- Get the schema right early — clear entities, honest constraints, and room to grow without painful rewrites.
- Protect access boundaries
- Scope access to roles by design, so people can see and change only what they should.
- Build useful interfaces
- Put the right view in front of each person — interfaces that make the data easy to read and act on.
- Make data trustworthy and reusable
- Validate at entry and keep data well-described, so it stays reliable and ready for analytics and AI.
Core stack
My core stack is built around Python, PostgreSQL, FastAPI, Next.js, TypeScript, Supabase, Docker, and cloud deployment workflows.
- Python
- TypeScript
- SQL
- PostgreSQL
- Supabase
- pandas
- NumPy
- FastAPI
- REST APIs
- Alembic
- Next.js
- React
- Tailwind CSS
- scikit-learn
- PyTorch
- XGBoost
- model evaluation
- Hugging Face
- sentiment analysis
- text classification
- chatbot workflows
- Vercel
- Render
- AWS basics
- Docker
- Docker Compose
- Git
- GitHub
- KoboToolbox
- ODK
- CKAN
- FAIR workflows
- metadata
- Streamlit
- Matplotlib
- Excel
- reporting dashboards
What I’m open to
I’m glad to talk about:
- Data Systems Engineer roles
- Data Scientist roles
- ML Engineer roles
- Research software / research data systems work
- Analytics dashboards
- Data infrastructure projects
- AI-ready workflow projects