Overview
I support research and health innovation teams that need stronger analytical workflows around biological and clinical data. This service sits at the intersection of data science, reproducible research, and health-domain problem solving.
The focus is practical: improve data quality, structure the analysis pipeline, document assumptions, and produce outputs that are easier to validate, communicate, and reuse.
Areas of support
- Clinical and observational dataset preparation
- Reproducible analysis pipelines in Python or R
- Feature engineering for structured biomedical data
- Exploratory analysis for cohorts, outcomes, and risk factors
- Support for omics-oriented workflows and downstream interpretation
- Visualisation and reporting for researchers and decision-makers
- Prototype decision-support analytics for health applications
Typical deliverables
- Cleaned and documented analysis-ready datasets
- Reproducible notebooks or scripts
- QA checks and data dictionaries
- Statistical or ML exploration reports
- Visual summaries for presentations, papers, or internal reviews
Ideal clients
- Research groups and academic labs
- Digital health or medtech startups
- Clinical innovation teams
- Organisations building analytics around biomedical or patient data
Best fit
This service is a good fit when you need someone who can bridge rigorous analytical work with real-world project delivery, especially in settings where health data, interpretability, and reproducibility matter.