Skills map

Skills

My skills begin with biomedical science in practice, then extend into the data, evidence and implementation work built around it. That includes supervised laboratory methods, diagnostic reasoning, clinical informatics, data interpretation, evidence synthesis and clear writing.

In practice

Core skill areas

Each skill is tied to work I have done, the outputs it supported and why it matters for biomedical and healthcare work.

Skill

Laboratory and diagnostic practice

Where I used it: Supervised and assessed degree practicals across microbiology, DNA manipulation, diagnostic pathology, anatomy, histology and biochemistry.
What it supported: Microbial isolation and identification, PCR and clone-screening workflows, indirect ELISA, H&E and cytology staining, microscopy measurements, enzyme assays and protein-purification work. Equipment included micropipettes, a thermocycler, gel apparatus, a microcentrifuge, a plate reader and light microscopes.
Why it matters: A laboratory result is only useful when the sample handling, method, controls, measurements and limits remain clear.

View laboratory practice

Skill

Clinical informatics and operational data

Where I used it: Clinical Informatics trial-operations project using a synthetic protocol, linked relational tables and an interactive dashboard.
What it supported: Protocol-to-data mappings, record-level validation issues, site and study metrics, reproducible reports and dashboard views.
Why it matters: Operational analysis is more useful when every summary can be traced back to the data relationships, rules and affected records.

View Clinical Informatics project

Skill

Biomedical data analysis

Where I used it: Final-year project workflows, dose-toxicity extraction and cleaning, quantitative pharmacology and genetics/statistical reasoning.
What it supported: Feature-reasoning summaries, model-evaluation interpretation, cleaned tabular outputs and documented limitations.
Why it matters: Biomedical data work is strongest when the question, source limits and interpretation rules are clear before analysis begins.

Read final-year project

Skill

Healthcare commercial strategy

Where I used it: Health Innovation East placement, QOF/adoption work, PulseOn strategy material and Charco/CUE1 business-case support.
What it supported: Evidence summaries, adoption logic, pathway framing, stakeholder decision-driver summaries and value reasoning.
Why it matters: Good strategy work has to connect clinical need, evidence quality, market context and the realities of adoption.

View Health Innovation East case study

Skill

Evidence synthesis

Where I used it: HIE evidence briefs, market-analysis material, final-year dissertation framing and toxicology data mapping.
What it supported: Structured evidence tables, limitations-first summaries, source-to-claim mapping and clear written recommendations.
Why it matters: Evidence only helps when the strength, limits and relevance of each claim are visible.

Read final-year project

Skill

AI/MedTech governance

Where I used it: AI Toolkit, final-year project governance handling, exploratory methods boundaries and AI-ECG adoption logic.
What it supported: Practical guidance-support material, governance framing, claim limits and adoption questions.
Why it matters: AI health tools need more than technical performance. They need evidence, safety, data governance, interoperability and monitoring.

View AI/MedTech Toolkit

Skill

Market intelligence and competitor analysis

Where I used it: CB Insights-supported market intelligence, secondary research and competitor-analysis materials across health technologies.
What it supported: Structured company lists, trend summaries, competitor matrices and adoption-readiness framing.
Why it matters: Commercial decisions need a clear view of who else is active, what evidence they have and how mature the space is.

View market intelligence case study

Skill

Writing and communication

Where I used it: Guidance-support material, public case studies, dissertation writing, module assessments and presentation-style internship outputs.
What it supported: Concise summaries, advisory material, case-study narratives, limitations sections and decision-focused tables.
Why it matters: The work only lands if a reader can see the point, trust the limits and understand what should happen next.

View selected documents

Page updated 12 August 2026